This article outlines the fundamentals of Retrieval-Augmented Generation (RAG) and illustrates how your data can be integrated into applications supported by Large Language Models (LLMs).

Retrieval-Augmented Generation (RAG)

Retrieval-augmented generation (RAG) entails recovering specific data from an indexed dataset and retaining a Large Language Model (LLM) to generate an answer to a given question or task. At a high level, this process involves two main components:

• Retriever: This component recovers relevant data based on the user-provided query.
• Generator: The generator augments the retrieved data, typically by framing it within a prompt context, and then feeds it to the LLM to generate a relevant response.

History of RAG

A research article titled “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,” written by scientists from Facebook AI Research, University College London, and New York University, is where the term “Retrieval-Augmented Generation” first appeared.

This paper presented the concept of RAG and demonstrated how it could be utilized in language generation tasks to produce more precise and accurate outputs. This work bids several positive societal benefits over earlier work: the fact that it is more strongly grounded in truthful knowledge (in this case, Wikipedia) makes it ‘hallucinate’ less with more factual groups and offers more control and interpretability,” the paper stated.

Additionally, the research highlighted that RAG could be employed in a wide variety of scenarios with direct benefit to society, for example, by endowing it with a medical index and asking it open-domain questions on that topic or helping people be more effective at their jobs.

RAG Architecture

RAG architecture consists of several core components that enable its functionality. These components include:

1. Web Server/Chatbot:

The web server hosts the chatbot interface, where users interact with the language model. Users’ prompts are passed to the retrieval model.

• Knowledge Base/Data Storage:

This component contains files, images, videos, documents, databases, tables, and other unstructured data that the LLM processes to respond to user queries.

• Retrieval Model:

The retrieval model analyses the user’s prompt using natural language processing (NLP) and seeks relevant data in the knowledge base. This data is then forwarded to the generation model.

• Generation Model:

The generation model routes the user’s initial prompt and combines the information collected by the retrieval model to generate a response, which is then sent back to the user via the chatbot interface.

Use Cases of RAG

• Customer Service: Many companies use RAG to enhance customer service by reducing wait times and improving the overall customer experience. Giving customer service professionals access to the most relevant company data can resolve issues more quickly.
• Master Data Management (MDM): RAG can greatly impact MDM by improving inventory management, consolidating sourcing contracts, and predicting inventory utilization. It also helps address customer and supplier management challenges, especially for B2C and large corporate clients with multiple legal entities.
• Operations: In operations, RAG can help speed up recovery from outages, prevent production line issues, and enhance cybersecurity measures. It also supports the adoption of AI across various operational areas, including IT operations and IoT, by utilizing data for more efficient problem-solving.
• Research/Product Development: RAG can speed up product development by identifying critical features that will significantly impact the market. It also helps engineering teams find solutions more rapidly, accelerating innovation.

How People Are Using RAG

Retrieval-augmented generation (RAG) allows users to converse with data repositories, creating new and dynamic experiences. This expands RAG’s potential applications far beyond the existing datasets.

For instance, a generative AI model enhanced with a medical index could be a valuable assistant for doctors or nurses. Similarly, financial analysts could benefit from an AI assistant connected to market data.

Nearly any business can transform its technical or policy manuals, videos, or logs into knowledge bases to enhance LLMs. These resources can be used for various purposes, such as customer or field support, employee training, and improving developer productivity.

Due to its immense potential, RAG is being adopted by organizations such as IBM, Glean, Google, AWS, Microsoft, Oracle, Pinecone, and many more.

Building User Trust

Retrieval-augmented generation (RAG) enhances user trust by providing models with sources they can cite, similar to footnotes in a research paper, allowing users to verify claims. This approach also helps models clarify ambiguous user queries and reduces the likelihood of making incorrect guesses, known as hallucinations.

Additionally, RAG is faster and more cost-effective than retraining a model with new datasets, allowing users to swap in new sources as needed easily.

RAG Challenges

While Retrieval-Augmented Generation (RAG) is a highly useful approach to AI development, it comes with several challenges. One of the primary challenges is the need to build an extensive knowledge base of high-quality content for reference.

Creating this knowledge base is complex because the data must be carefully curated. Low input data quality will negatively impact the accuracy and reliability of the output.

Additionally, developers need to address any biases or prejudices that might be present in the knowledge base.

Finally, while RAG can enhance reliability, it cannot completely eliminate the risk of hallucinations. End users still need to exercise caution when trusting the outputs.

Pros and Cons of Retrieval-Augmented Generation

RAG is a powerful tool for organizations. Below, we’ll examine some of its most notable benefits and drawbacks.

Why Indium

As a Databricks consulting partner, Indium brings over a decade of expertise in maximizing enterprise data potential. Leveraging Databricks’ robust, flexible, and scalable platform, our services span the entire data analytics spectrum, ensuring seamless integration and management.

Our accelerator, ibriX, enhances data integration and management capabilities, accelerating your enterprise’s data transformation. Our services include Databricks consulting, cloud migration, lakehouse development, data engineering on Databricks, and advanced analytics, AI, and ML solutions.

Working with Indium guarantees a thorough approach to data transformation, utilizing state-of-the-art tools and customized solutions to advance your company. For more details, reach us.

Wrapping Up

Retrieval-augmented generation (RAG) is a valuable technology for enhancing an LLM’s core capabilities. With the right knowledge base, organizations can equip users with access to a wealth of domain-specific knowledge.

However, users must remain proactive about fact-checking outputs for hallucinations and other mistakes to prevent misinformation.

FAQs

The post Maximizing Data Potential: Retrieval-Augmented Generation with Large Language Models (LLMs) appeared first on Indium.

Is it enough to hyphenate the words work and life to achieve the envisioned balance? At Indium, it is believed that this hyphenation is a constant practice, combining work and individual well-being to always keep the envisioned balance undisturbed.

How is well-being prioritized at Indium?

• Ever since the pandemic’s onslaught in 2020, Indium has embraced the Hybrid model of work and scaled effortlessly in the new normal. Although the pandemic came and did what it could to us, we chose to continue with the Hybrid model. Today, we are 3000+ Indiumites, with almost 70% of our talent force working remotely and the remaining 30% working in a hybrid mode.
• Although our work mode is hybrid, we endeavored to make our workplaces much more vibrant, peppy, and positively charged, with an atmosphere of affable collaboration and people engagement.
• As our people switch intermittently between working from the office, working from remote, and working hybrid, we realize that by combining our capabilities in these different modes, we have delivered all our envisioned business solutions.
• We believe that combining our capabilities from the different work modes helps create empowered teams at work, but we also need something extra to create ‘United Teams of Indium’: promoting the collaboration culture as a team.
• Well-being reaches full circle when mental health is prioritized. We have enabled 1-to-1 helpline services that can assist in resolving anything that is a reason for stress.
• There is a continuous effort to keep adding on the binding factors that help our people come together as a team, such as:

1. Individualizing and expanding the scope of appreciation and recognition at work in more forums such as monthly, quarterly, half-yearly & annual R&Rs, and also for spot occasions that enable instant recognition.

2. Enriching the onboarding, learning & engagement ecosystems by offering more interaction-led & interactive journeys for perceptibly relatable people experience

3. Consistent communication of policy updates & new benefits; socializing the communication through focused group discussions and extended calls with different practice lines and extended teams.

4. Individual attention to some outstanding success stories or unique achievements of our people through social handles.

5. We make every employee event a celebration, whether town halls, talent display events, or smaller get-togethers and ethnic occasions.

Is this the end of the whole story? There is still a long way ahead, and we aim to make it a never-ending tale filled with an unceasing timeline of effort to bring immeasurable delight and prioritize the overall well-being of every single Indiumite.

The post Work-Life Balance at Indium: Prioritizing Well-being appeared first on Indium.

But here’s the thing: with great power comes great responsibility (cue Spiderman), and in the world of LLMs, that translates to making sure they work as intended and don’t go off the rails. That’s where LLM testing comes in. Imagine you built an LLM that writes product descriptions. You wouldn’t want it accidentally generating gibberish or, worse, offensive content, right?

LLM Testing helps you identify these issues before they reach your customers. Industry analysts like Gartner and Forrester predict that by 2025, 70% of organizations will be using some form of AI, and a significant portion of that will involve LLMs. So, understanding how to test them is becoming an increasingly valuable skill.

This blog will be your guide to the wild world of LLM testing. We’ll break down the different types of tests, explore the challenges you might face, and equip you with best practices to ensure your LLMs are up to snuff.

Why is LLM testing different?

Testing LLMs throws a curveball at traditional software testing methods. LLMs are inherently unpredictable, unlike your typical software application, which produces predictable outputs. They’re trained on massive datasets of text and code, and their responses can vary depending on the input and the context. Think of it like asking a friend for a restaurant recommendation. They might suggest Italian one day and Thai food the next, depending on your mood and what they recently ate.

This non-deterministic nature of LLMs makes it tricky to write tests that say, “If I ask for a summary of this article, the output should be exactly X characters long.” That exact match approach just won’t work.

Here’s a stat to consider: a McKinsey report estimates that up to 80% of the value delivered by AI comes from non-technical factors like effective human oversight and testing. So while building a powerful LLM is important, making sure it works as intended is equally crucial.

The challenges of LLM testing

Unlike traditional software testing, LLM testing comes with its own set of challenges. Firstly, their non-deterministic nature means they can produce varying outputs for the same input, complicating the creation of fixed test cases. Secondly, LLMs operate as black boxes, concealing their inner workings, which impedes efforts to identify the sources of errors. Additionally, the cost associated with testing LLMs can be significant, particularly for non-open source models where expenses rise in tandem with the number of queries used for testing. These factors collectively contribute to the complexity and expense of ensuring the reliability and accuracy of LLMs through testing procedures.

The testing toolbox: Functional, performance, and responsibility testing

Let’s delve into the different types of tests you can use for your LLM:

• Unit Testing: The foundation of LLM testing is unit tests that evaluate an LLM’s response to a specific input based on predefined criteria. Imagine testing an LLM to summarize news articles. A unit test would assess if the summary captures the main points and avoids factual errors.
• Functional Testing: This involves evaluating an LLM’s performance on a particular task, like text  summarization or code generation. It essentially groups multiple unit tests to assess the LLM’s proficiency across a specific use case.
• Regression Testing: As you iterate and refine your LLM, regression testing ensures that changes haven’t introduced any unintended consequences. It involves running the same set of tests on different versions of the LLM to identify potential regressions.
• Performance Testing: Here, the focus isn’t on the correctness of the output but rather on the LLM’s efficiency. This includes metrics like tokens processed per second (inference speed) and cost per token (inference cost). Optimizing performance is crucial for cost-effectiveness and real-time responsiveness.
• Responsibility Testing: This ensures your LLM adheres to ethical principles and avoids generating biased or offensive content. Bias metrics and toxicity metrics can be used to identify and mitigate these issues.

Metrics for measuring LLM performance

Various metrics are used to measure LLM performance during testing. Some of the common ones include:

Frameworks, metrics, and automation: Best practices for LLM testing

Now that you understand the different types of tests, let’s explore some best practices to ensure your LLM testing is effective:

Frameworks like DeepEval offer a suite of tools and metrics specifically designed for LLM testing. These tools can streamline testing and provide valuable insights into your LLM’s performance. Choosing the right evaluation metrics is crucial. Traditional metrics like BLEU score, which focuses on n-gram overlap, might not be ideal for complex tasks like summarization. Consider using more sophisticated metrics like G-Eval or QAG that evaluate semantic similarity and factual correctness. Integration with CI/CD pipelines allows you to automate your LLM tests, ensuring they are run every time you make a change to the model. This helps catch regressions early and prevents bugs from slipping into production.

Test your LLM with data that reflects real-world scenarios. This helps identify potential issues that might not surface with synthetic data sets. While automation is essential, human judgment remains invaluable. Involving human evaluators can help assess aspects like coherence, readability, and overall user experience.

Energy shots! The future of LLM testing

We’re not revisiting the challenges here. I’m simply emphasizing the importance and the opportunities!

LLM testing is a critical step towards ensuring these powerful models’ responsible and ethical deployment. By employing a multi-layered testing approach, leveraging the right tools, and staying informed about emerging trends, we can build trust in LLMs and unlock their full potential to revolutionize various industries. 

Is there anything specific you’d like to delve deeper into regarding LLM testing? I can provide more details on a particular testing approach or explore the ethical considerations surrounding LLM development. 

The post Are You Ready to Test Large Language Models? Embracing the Unpredictable! appeared first on Indium.

Large Language Models (LLMs) have taken the world by storm. Their ability to generate human-quality text, translate languages, write different kinds of creative content, and answer your questions in an informative way is nothing short of remarkable. However, beneath this veneer of fluency lies a hidden challenge – hallucinations.

What are LLM hallucinations?

Imagine you ask an LLM, “What is the capital of France?” It confidently replies, “Madrid.” This is a classic example of an LLM hallucination. Hallucinations are factually incorrect or misleading outputs generated by the model, often woven seamlessly into a seemingly coherent response.

These hallucinations can be particularly dangerous because they can be delivered with an air of believability. Unlike a random string of nonsensical words, hallucinations are crafted based on the LLM’s vast knowledge base, making them difficult to detect for the uninitiated user.

The extent of the problem

A recent report by Gartner predicts that by 2025, 30% of customer service interactions will leverage LLMs. This rapid integration into mission-critical applications underscores the urgency of addressing LLM hallucinations.

A 2023 study by McKinsey found that 60% of businesses surveyed expressed concerns about the potential for misinformation and bias in LLM outputs. This highlights the need for robust metrics to not only identify hallucinations but also understand the root causes behind them.

Why do LLMs hallucinate?

LLMs are trained on massive datasets of text and code. While impressive in its scale, this data can be inherently imperfect, containing factual errors, biases, and inconsistencies. The LLM, lacking the ability to discern truth from falsehood, simply absorbs it all. When prompted to respond, the model may unknowingly draw upon these inaccuracies, leading to hallucinations.

Another factor is the statistical nature of LLM outputs. LLMs predict the next most likely word in a sequence, which can lead them down a path of creative embellishment, straying further from factual accuracy with each step.

The metrics maze: Measuring the unmeasurable?

Evaluating LLM performance is a complex task. Traditional metrics like BLEU score, which assess similarity between generated text and reference outputs, fail to capture the nuance of factual correctness.

New metrics are emerging to address this gap. Here’s a breakdown of some promising approaches:

Statistical scorers: These metrics, like perplexity, measure the LLM’s confidence in its predictions. Higher perplexity might indicate a higher chance of hallucination, but it’s not a foolproof indicator.

Model-based scorers: These metrics leverage pre-trained models or other LLMs to evaluate factual consistency. For instance, ChainPoll utilizes human experts to create reference responses, allowing for a more nuanced assessment of factual accuracy.

LLM-eval scorers: These innovative approaches use an LLM itself to assess the outputs of another LLM. G-Eval, for example, employs an LLM to generate evaluation steps and a form-filling paradigm to determine a score. While powerful, such methods can be susceptible to the limitations of the evaluating LLM itself.

Hybrid scorers: These metrics combine elements of statistical and model-based approaches. BERTScore and MoverScore are examples, using pre-trained models to compute semantic similarity between the LLM output and reference texts.

The road ahead: Mitigating hallucinations

There’s no silver bullet for eliminating LLM hallucinations entirely. However, a multi-pronged approach can significantly reduce their occurrence. Here are some key strategies:

Data quality: Curating high-quality training data sets that are factually accurate and diverse can significantly improve LLM performance.

Prompt engineering: Crafting clear and concise prompts that guide the LLM towards generating factual outputs is crucial.

Model fine-tuning: Fine-tuning LLMs on specific tasks and datasets can help them specialize in areas where factual accuracy is paramount.

Human-in-the-loop systems: Integrating human oversight into LLM workflows can ensure the final output is vetted for accuracy before being presented to the user.

Beyond hallucinations: A broader look at LLM trustworthiness

While hallucinations are a major concern, they represent just one facet of LLM trustworthiness. Here are some additional considerations:

Bias: If LLMs are trained on data reflecting societal prejudices, they may unknowingly inherit those biases and generate outputs reinforcing them. To prevent this, we need to ensure training data is balanced and accurately represents the world we live in.

Explainability: Understanding how LLMs arrive at their outputs is essential for building trust. Research into explainable AI (XAI) techniques is ongoing to address this challenge.

Transparency: Open communication about the limitations and capabilities of LLMs is essential for managing user expectations and fostering trust.

The future of LLMs: A collaborative dance

LLMs hold immense potential to revolutionize various industries. However, addressing the challenge of hallucinations and building trust in these models is paramount. This requires a collaborative effort between LLM developers, data scientists, ethicists, and policymakers.

Here’s a glimpse into what the future might hold:

Standardized benchmarks: The development of standardized benchmarks for evaluating LLM factuality and trustworthiness will be crucial for ensuring consistent and reliable performance.

Regulatory frameworks: As LLM applications become more widespread, regulatory frameworks may emerge to establish guidelines for data quality, bias mitigation, and explainability.

Human-AI collaboration: The future likely lies in a collaborative approach where humans and LLMs work together, leveraging each other’s strengths to achieve optimal outcomes. Humans can provide guidance and oversight, while LLMs can automate tasks and provide insights at scale.

Conclusion

LLMs are powerful tools with the potential to transform our world. By acknowledging and addressing the challenge of hallucinations and other trust-related concerns, we can pave the way for responsible development and deployment of these transformative technologies. In this collaborative dance between humans and AI, LLMs can become powerful partners, augmenting our intelligence and creativity while ensuring factual accuracy and ethical considerations are at the forefront.

The post The hallucination factor: Why metrics matter in the age of large language models (LLMs) appeared first on Indium.

As we evolve with the latest technology trends like AI and ML, IoT, Blockchain, Digital Twins, etc., the questions that come to mind are:  

1. How do we keep pace with these changes while releasing software to the market with fewer bugs and improved quality?

2. How can we incorporate continuous testing and delivery?  

SHIFT-LEFT TESTING

Indium’s TestOps team adopts a Shift Left approach to testing. The term ‘shift-left’ suggests a chronological progression to the left on the timeline of the SDLC, as indicated below. This approach aims to find and address defects as early as possible in the SDLC process, reducing the cost and impact of fixing issues later in the lifecycle.  

In short, the mantra is to test early and often. With this approach, businesses can release new features faster, as testing is less likely to restrain development. However, according to a recent survey, 51% of businesses are at a disadvantage in responding to vulnerabilities because they use manual processes, leading to an insurmountable vulnerability backlog. 

We simplify operations across the following key areas: Test Environment Deployment and Management, Validation, and Test Data Management and Monitoring. This ensures rapid release while reducing bugs by automating CI, QA, and continuous deployment (CD). Successful shift-left testing goes hand in hand with test automation. But how do we ensure continuous integration and continuous delivery (DevOps) with test automation? 

TestOps entails a set of practices to ensure that products and services meet specific quality standards. This encompasses testing, monitoring, and analyzing the performance of systems and applications to identify and resolve any issues or bugs. The primary objective of TestOps is to guarantee that products and services are reliable, functional, and aligned with user needs. It plays a critical role in the software development, ensuring timely product delivery that fulfills customer requirements. 

We have a dedicated practice to streamline your Dev-Test-Ops cycle that maximizes operational efficiency with improved product quality and faster time to market! 

Drawing upon decades of experience in digital assurance, I will delve into pivotal questions: the rationale behind automating testing in the DevOps lifecycle, selecting test cases and constructing automation flows, and the criteria for identifying the optimal DevOps software testing tool. 

Automated Software Testing: The Glue Between Dev and Ops 

A shift to a built-in quality mindset is essential to succeed in DevOps and maintain rapid software delivery. Collaboration, training automation engineers as testers, and recognizing the value of test automation are key. DevOps testing aligns seamlessly with agile and CI/CD, aiming for flexibility, velocity, and quick, high-quality releases. Automation speeds up the release pipeline, particularly in testing, reducing delays and errors. Automation, especially for tasks like regression testing, frees testers for higher-value, human-centric activities. The result is an optimized and efficient software delivery process. 

Optimizing Test Cases and Enhancing DevOps Test Automation Workflows 

As the EVP of Digital Assurance, delving into the practicalities of implementing test automation within our DevOps framework is both an exciting and strategic endeavor. To seamlessly integrate automated testing into our dynamic DevOps lifecycle, a meticulous approach to our release pipeline is paramount. Here’s a breakdown to guide us on this journey: 

• Understanding Our Stages: We must comprehensively understand the key stages embedded in our release process. This foundational awareness will be a bedrock for the subsequent steps in our automation journey.

• Gate Check for Progression: Identifying crucial gates and delineating requirements are pivotal for ensuring fluid progression from the initial build to the final production stage. This strategic checkpoint will fortify the reliability and resilience of our release pipeline.

• Building a Feedback Loop: Establishing effective feedback mechanisms is key to swift error detection and resolution. As we craft our automation flows, integrating robust feedback loops will be instrumental in maintaining high software quality throughout the development lifecycle.

• Crafting an Operational Checklist: To streamline our release cycle, I propose compiling a detailed operational checklist encompassing all procedures, services, and actions. This checklist will serve as a comprehensive guide, ensuring that every aspect of our operations aligns seamlessly with our overarching automation goals.

How it works?? 

• DevOps Integration: QA Ops ensures seamless integration of DevOps by incorporating all necessary testing frameworks and tools into the product development lifecycle pipeline. 

• Enhanced Test Planning: QA Ops provides a centralized platform, simplifying test planning for both testers and developers. It facilitates the identification of which tests to write and when to execute them. 

• Test LifeCycle Management: The status of each Test significantly influences its treatment within build automation systems, such as CI/CD. This integration ensures a cohesive approach throughout the testing lifecycle. 

• Test Version Control: Implementing processes that guarantee changes to tests undergo thorough review and approval, leveraging capabilities like pull requests in code. This ensures the reliability and stability of the testing process. 

What transpires between these bookend phases?  

The cornerstone is software testing, which is diverse in nature but vital in function. Integration tests ensure that modifications or new features are added without affecting the application. Usability testing uncovers previously unknown faults in code design, preventing problems from reaching end users. Device compatibility tests guarantee that the code works as intended in real-world scenarios, accounting for  the complexities of numerous hardware and software factors. 

This demonstrates why software tests serve as the glue that binds developers’ code to the production-level application maintained by TestOps engineers. 

Our TestOps team build testing solutions that support and reinforce DevOps aims in order to properly incorporate software testing into a continuous delivery pipeline. When selecting a testing platform, we check for the following features: 

• Support for an Array of Frameworks: Our evolving development demands may necessitate a testing solution that supports a wide range of frameworks, guaranteeing adaptability in our continuous delivery pipeline. 

• Scalability: The platform for testing should scale effortlessly, performing tests as soon as possible, and supporting parallel tests to meet our evolving requirements. Cloud-based solutions offer the scalability required for our dynamic pipeline. 

• Quick Testing: To prevent delays, tests must be performed swiftly. Utilizing large-scale parallel tests and prioritizing emulator and simulator compatibility testing can expedite the process. 

• High Automation: The testing solution is at the heart of DevOps and should seamlessly integrate with our automated toolset, allowing triggered tests, automated result analysis, and information sharing amongst the organization. 

• On Demand Testing: Performing tests whenever necessary is crucial. Cloud based testing provides a cost-efficient solution, avoiding the inefficiencies associated with maintaining an on-premises testing environment. 

• Security: Security features within the testing platform, such as encrypted test data and robust access control, are paramount in ensuring the entire team, including testers, contribute to keeping our applications secure. 

Incorporating these qualities into our testing strategy empowers our DevOps + QA teams to collaborate efficiently. This ensures the reliability and stability of our production code across environments, maximizing the scalability, visibility, agility, and continuity of our software delivery pipeline as we embrace the full potential of a DevOps-based workflow.

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Celebrating achievements is an integral part of our day-to-day operations at Indium. We firmly believe in acknowledging excellence in its myriad forms,

•  Whether it’s hitting significant milestones,
•  Embodying our company’s core values,
•  Exceeding expectations,
•  Or showcasing steadfast long-term commitment.

To ensure that recognition permeates our culture palpably, we’ve implemented meaningful platforms such as

• Tuzo – Tuzo is a platform dedicated to recognizing the achievements and contributions of peers, colleagues, and friends.
• Rewards & Recognitions – Our Rewards and Recognitions program plays a crucial role in keeping employees motivated and enthusiastic about generating new ideas and innovative solutions. When employees are rewarded and recognized, it not only helps them surpass their own performance but also inspires their teammates to excel.
• Spot Awards – Spot Awards are designed to acknowledge unique contributions achieved within a short timeframe. These awards let employees know that their exceptional efforts have been noticed and appreciated.

These platforms serve as immediate avenues for acknowledgment and appreciation, enabling us to commemorate successes promptly and reinforce the behaviors and attitudes we hold in high regard.

Yet, our approach to employee recognition transcends the confines of formal programs. We empower each member of our organization to actively engage in recognizing their peers, fostering inclusivity and a sense of belonging. Every gesture counts, whether a heartfelt thank-you note, a commendation in a team meeting, or a simple appreciation shared during a casual coffee session.

As our company continues to evolve and expand, nurturing a culture of recognition remains our paramount objective. We recognize that what motivates one team member may not necessarily resonate with another. Therefore, we invest time and effort into understanding individual preferences, tailoring our recognition initiatives accordingly. In doing so, we cultivate a more profound sense of belonging and drive collective success, ensuring that every achievement, regardless of its magnitude, is commemorated in a manner that holds significance and meaning.

The post Celebrating Employee Achievements: Spotlight on Success Stories appeared first on Indium.

As consumers are constantly introduced to new products and technology options, the need to be careful in developing and designing the product is high.

So now, returning to the question, what purpose do you think of when designing a product? It has to be customer experience, the intangible result that crosses all touchpoints while a user experiences the product.

Discover quality engineering practices for customer experience, Indium

Software’s approach and future insights are discussed in this blog.

Introduction to Quality Engineering 

The effort to initiate cutting-edge technology or introduce technological advancement can be replaced by adding elements like increased efficiency, performance optimization, and security improvements that help create a product focusing on customer experience (CX) and satisfaction.

Quality engineering practices are the only recommendations one can make. Quality engineering is a systematic process that deals with the beginning stages of product development through the final stage of product delivery.

As quality engineers act as the front-line creators of any product, from designing to developing, the practices they render help with the usability and accessibility of the product, along with product cost, quality, and the organization’s bottom line.

Gain Insight into the Customer Experience 

Every company aims to crack the most difficult aspect of business: gaining customer loyalty and a positive consumer response. If companies achieve this, they get a free marketing spree as positive word-of-mouth exchanges impact the business profitably in the long run. But they all fail to accomplish this milestone as the focus is not on how customers feel about the product or service but on how much revenue the product will generate when it hits the market.

Re-arranging the said approach can make you the king of business for consumers to decide who and which product made them feel connected and enhanced the experience. First, let’s analyze and find the factors influencing Customer Experience (CX) that can take your business to new heights.

Slow and Unresponsive Experience is Your Primary Pitfall

We all purchase products or applications to guide and assist us in times of emergency and helpful situations. Imagine the product being sluggish and taking time to respond to a very small activity. So don’t drive customers to frustration with design and development that don’t support and respond to issues promptly.

Inefficient and Complex Design Ruin Customer Convenience

To accomplish work through a platform or application requires a minimum understanding of the product. A user-friendly and intuitive design is essential to transcending tasks effortlessly. A smooth and frictionless approach is required to bring customers back. So build applications that reduce the effort required to navigate and engage with the brand.

Vulnerabilities and Data Threat demolishes the Loyalty of Customers

A big no from consumers happens when they realize their privacy is being compromised. Any application or product that doesn’t stand against security breaches will never be in customer word-of-mouth referrals. Building confidence in customers and earning loyalty can be done only through a robust infrastructure. So build products that reduce unauthorized access and foster trustworthiness among customers.

Delayed Assistance and Technical Support Have Negative Impact

To render support to consumers and extend any service if needed, you drive them back to the brand. If a product fails to work or consumers find it difficult to operate, customer support should offer help and ensure the doubt has been cleared. Excellent and polite customer service is necessary to retain customers in the long run. So build long-term relationships with customers with good communication and support.

Quality Engineering Approach Towards Customer Experience

Quality Engineering is a methodical approach that companies widely recognize in today’s business environment to meet customer requirements and satisfaction. It involves utilizing techniques and tools to shape a product or service according to customer expectations while focusing on cost efficiency, waste reduction, and other factors crucial to its success.

Quality engineers play a pivotal role as the primary critics and decision-makers throughout the product or service design and development process. From clearly defining product requirements to enabling automation testing, continuous integration, deployment, and code review, to monitoring and analyzing the product’s scope, performance, and customer experience, practicing quality engineering for every product or service is essential and demanding.

Develop a thorough understanding of the techniques and tools utilized in quality engineering.

Quality Assurance – Focuses on precise procedures and eliminates process variation.

Quality Control – Test the sampling until it meets design specifications to avoid potential defects in the production process.

Six Sigma – A data-driven technology that analyzes the root cause of defects and eliminates them in the production phase.

Quality by Design – The method emphasizes incorporating quality standards into the design from the start.

Taguchi Method of Quality Control – Customer experience and cost-effective models are highlighted through statistical experimentation and optimization techniques.

Quality Risk Management – The approach aims to plan extensively to identify potential risks and develop preventive measures that can improve product quality and standards.

Reliability Engineering – The application of engineering techniques and statistical analysis to develop methods to cope with failures that do occur.

A Closer Look on CX and Quality Engineering

Indium Software’s Approach to Customer Experience

At Indium Software, we accelerate time-to-market through quality engineering practices. By carefully implementing design principles, development methodologies, and automation techniques, it is possible to build products or applications that meet consumer expectations as they use various applications. As we forge ahead in our software development, the following services help us build applications that stand out among consumers for their usability, performance, and privacy.

Data c – Protect your data from unauthorized access and security breaches with our data assurance services.

API/Micro Service – Deliver more responsive and smooth customer interactions through our well-defined microservice architecture.

Low-code Platform Testing – Build the application with minimal coding and evaluate the functionality easily before deploying.

TestOps – Create a more effective and efficient application and achieve seamless development integration with continuous automated testing.

Smart Assistant Testing – Provide reliable responses and increase the quality of the application with smart and virtual testing assistants.

Discover The Future Landscape of CX in Quality Engineering

As innovations and technologies grow, the urge to build products and meet customer expectations will be stronger than innovation.

As consumers become aware of the latest advancements, the need to provide applications or products as per their expectations can be met only through quality engineering and its practices. In the future, quality engineers will primarily focus on customers’ insights before determining other parameters.

From hyper-personalization to AI-driven testing methodologies to voice gesture-based interfaces to the Internet of Things to emotional analytics, the customer experience, customer behavior, customer emotions, and customer loyalty can all be met and maintained with future quality engineering practices.

As businesses strive to differentiate themselves in a competitive market, quality engineering will be a key enabler in delivering delightful and memorable experiences that foster customer loyalty and advocacy.

Wrap-Up 

In the fiercely competitive business landscape, customer engagement and retention are set to become top priorities in the coming years, driven by continuous technological advancements that demand a profound connection between customers and products or applications in terms of experience, expectations, and satisfaction.

Quality engineering enables companies to craft products and applications that align with rigorous quality engineering practices, facilitating easy measurement of product success and swift detection of any flaws that customers may not appreciate.

As the future increasingly revolves around technology, embracing quality engineering principles and leveraging relevant tools empowers organizations to elevate product quality, enhance customer satisfaction, and drive superior business performance.

The post How Customer Experience is Shaping Quality Engineering Practices appeared first on Indium.

At Indium, we strongly believe in nurturing employee’s career through:

•  Camaraderie,
•  Open communication and
•  Flat hierarchy

That provides a platform for collaborative work, leading to better learning and honoring everyone’s achievements that circumvent any stereotypes or biases. In short, we celebrate diversity and embrace inclusion that fosters our work environment, where employees can express themselves without fearing judgment.

We consciously recognize the value everyone brings in, irrespective of where they come from and what level they hold in the organizational chart. We build trust among the employees by demonstrating our true values of

• Empathy,
• Compassion,
• Teamwork, and
• The overall well-being of the employee.

We also ensure that every employee is empowered to make decisions and learn from mistakes. The diversity and inclusion culture promotes our values that align with the organization’s culture and goals. Furthermore, providing periodic and meaningful feedback and recognition along with engagement and fun activities enhances the employee’s steadfast commitment to delivering the best and boosts their morale. Our leadership has been our cornerstone in achieving the cohesive team that we are today with a diverse and inclusive work environment.

The sense of belonging is what made us a Great Place to work. We follow a hybrid model across the world, with multigenerational and multicultural employees who work together as one team. It is with pride that we say that a diverse population with an inclusive mindset has enhanced every employee’s career and fostered a sense of belonging.

The post Diversity and Inclusion at Indium: Fostering a Culture of Belonging appeared first on Indium.

In some cases, the solution needs to work with organizations who want to keep the Database separately from Mendix or want to use the existing database.

1. Solution

First, it is possible to build a Mendix application and to bring your own database. But the question is how your Mendix application can work seamlessly with your existing database tables & data. The solution(s) we are discussing and elaborating here is not applicable in case the existing application is being developed with a Microservices or Service Oriented Architecture, with this architecture mostly the database operations are handled using APIs.

There are ways Mendix can work with existing database tables, but how? Each of our problem statements is different, but it can be broadly classified and sectioned into a few questions that we need to answer and identify the right approach for our problem statement(s)

1.1 Questions

1. How do we want to provide access to the database?

• Full Control to Mendix
• Limited access to user roles, e.g. Read only
• Whether the production needs database administrator intervention to approve the DML statements before the application installation?

2. Are we going to share this database with other applications?

3. Are we migrating the application, which uses your current database, to Mendix?

4. Do you want to migrate Mendix database but a different database like MSSQL to Postgres using Mendix?

1.2 Indium Matrix for using Existing Database in Mendix

Below are the tools and techniques to choose from

1. Database Replication

2. Database Connector

3. Mendix Modeller Configuration

4. Database as a Web Service – REST, OData, SOAP

5. Mendix Platform SDK (Programmatic solution)

We at Indium Software put together our knowledge and experience with the simple matrix to choose the right option at given point in time based on the problem the customer faces.

2. Tools

2.1  Database Replication

2.1.1 About

You can use the Database Replication module to import data from existing databases into your Mendix application. You have the ability to specify the mapping for each table, column, and relationship to your Mendix domain model. Even complex mappings involving multiple table joins can be achieved. The configuration can be done either in the client or using Java.

2.1.2 Typical Use Cases

• Convert an existing database to a Mendix domain model.
• Integrate your programme with a database used by another programme.
• Create mappings between database columns and object attributes
• Map database references to Mendix object references
• Map object attributes based on multiple joined tables

2.1.3 Features

• Support for custom queries for object attributes
• Compatibility with SQL Server 2005 or later, Oracle, AS400, DB2, PostgreSQL, DMS2, and Informix database systems.
• Assistance with non-persistent objects
• Automatic query generation for object attribute values
• Object events are executed when importing

https://marketplace.mendix.com/link/component/160

2.1.4 Advantages

• You can configure how you want to import the data using multiple options which gives the flexibility.
• Easy to use.
• Reduce the efforts of migration.

2.1.5 Limitation

• If  you are using the Excel Importer, then  you will need Excel Importer 3.0 or higher when using this module
• Consumes lots of memory, since all the values need to be remembered to keep the track of all the key changes
• It commits the object even though there are no changes to trigger the events, which can overburden the app.

2.1.6 Database Sync Process (Optional)

The data is updated in both systems thanks to scheduled synchronization between your application and a database used by another application.

Typical use case, during the phases of migration of the application. We do not recommend keeping two copies of the same data. This will cause the data to be inconsistent. So, choose wisely.

2.2  Database Connector

GitHub Link: https://github.com/mendix/database-connector

2.2.1 About

The Database Connector allows for a quick connection to external DBs (databases), offering you the freedom to choose from a wide range of databases and SQL dialects. This enables you to integrate your external data directly into your Mendix application without any limitations.

The connector supports below functionality to execute queries at your databases: 

• Run (Execute) query – For executing SELECT queries and obtaining a list of objects as a result
• Run(Execute)  statement – For executing other DML commands and obtaining either an integer or long value indicating the number of rows affected.
• Run(Execute)  parameterized query – For executing SELECT queries with input parameters, resulting in a list of objects.
• Run(Execute)  parameterized statement – For executing other DML commands with input parameters and getting either an integer or long value representing the number of rows impacted.
• Execute callable statement – For executing a callable statement.

2.2.2 Prerequisites

These are the prerequisites for using this connector:

• A database connection URL address that points to your database
• The username for logging into the database, with respect to the database connection url address
• The password for logging into the database, with respect to the database connection url address
• Add necessary JDBC driver libraries (.jar files) in the userlib directory of Mendix application
  • For e.g., if Mendix app needs to establish the connection to the Cloud PostgreSQL database (jdbc:postgresql://<instance URL>:5432/postgres), we need to put the corresponding PostgreSQL JDBC driver .jar inside the userlib folder.
• Relevant to the Execute Query action: a domain model entity that can be utilized to hold the results of the executed query
  • For instance, if you have a query such as “select name, number from stock”, which has two columns (of string and integer data types respectively), to use the Execute Query action, you must add an entity in the domain model with attributes that match the columns in the query.

2.2.3 Advantages

• Database connector is maintained with single threaded, avoiding memory leakages
• Ability to connect to multiple databases in a single application (a composite microservices)

2.2.4 Limitation

• The parameterized actions are only available with Database Connector versions 3.0.0 and above. For these, it is necessary to use Mendix 8.6.0 and above.            
• You can face memory issues for large data sets.
• Doesn’t have any configurations on thread pool size, connection timeout, etc.

2.3  Mendix Modeller Configuration

These settings can be configured as follows:

• Studio Pro – To access the option to connect to a database in Studio Pro, go to the App Explorer, view the App, open Settings, edit a configuration, and check the Configuration tab. select either the Default Configuration or Active Configuration to display the option.

2.3.1 Prerequisites

• Type: Currently supported RDMS databases
• URL:  Database URL that points to your Database with the port. For example, if you want to connect to the Cloud PostgreSQL database, :
• Database Name: Your Initial Database
• Use Integrated Security, Applicable only for MSSQL (Microsoft SQL Server) Database
• The username and password for logging into the database, relative to the database URL address
• If the database connection requires a self-signed certificate to establish the connection, then add a Certificate in Certificates Tab

2.3.2 Advantages

• Easy to use connection can be achieved easily by proving the details.       

2.3.3 Limitation

• You can connect to the databases which are available in the list, else you need to use connectors.
• For Production in Mendix Cloud, only PostgreSQL is available.
• To use Integrated Security with MSSQL, the Mendix application should be deployed in Windows Server(IIS)

2.4  Database as a Webservice

2.4.1 About

Web Services provide a solution to the interoperability issue by enabling different applications to connect their data. With Web Services, you can transfer data between diverse applications and platforms. To allow Mendix to use an existing database, you can expose the required functionality as a Service, making it easily accessible by Mendix.

Mendix supports the most widely used web service standards, including SOAP, REST, and OData. However, creating a wrapper for an existing database to connect with Mendix may require additional effort. The recent trend towards Service-Oriented Architecture or Microservices promotes API-based connectivity, which is effortless and efficient in Mendix.

On the other hand, if the database is not being utilized by any other applications, it is recommended to use the Data Connector or Data Replication to fully leverage the capabilities of Mendix.

2.4.2 Mendix Data Hub

There are few advantages when using Database as a web service when it is exposed as OData. Mendix provides a premium service called Data Hub.

The Mendix Data Hub Catalog is a comprehensive and open metadata repository that is based on industry standards, allowing developers and business experts to find and explore data resources within their interconnected ecosystem.

Data Hub Connectors enable organizations to integrate their data sources with Data Hub, thereby enhancing the catalog and making the data available to developers. Connecting to data from Mendix applications, Siemens Teamcenter, SAP, and numerous other commonly used enterprise data sources can be done with ease.

Refer: https://www.mendix.com/data-hub/

With Mendix Data Hub you find all data that is available across your Company’s software landscape and use it in your Mendix projects.

Share Data between Mendix Apps – Use and edit Data Assets from one Mendix app in another.

Connect to Non-Mendix Apps – Build an OData wrapper around your non-Mendix App to connect.

Integrated in Studio (Pro) – Use the Data Hub Panel in Studio Pro to search and use for Data Assets.

2.4.2.1  How to use Data Hub

Search – Finding Connectable Data Sources

Users can find shared datasets by searching the Data Hub Catalog

Register – Sharing Datasets

To make the data from your apps accessible to others, you can publish the datasets as an OData service and register it in Data Hub. In a Mendix application, the datasets correspond to the Entity sets for a specified Entity

Consume – Using Registered Datasets

Assets that have been registered in the Data Hub Catalog can be utilized in the Mendix Studio Pro for app development. These external data sources are displayed in the domain model as external entities, which can be combined with local entities.

Curate – Maintaining Registered Assets

To make sure the right people find your service, you can edit app owners, add tags and descriptions, and toggle discoverability.

2.4.2.2  Advantages

• DataHub has versioning, so you can stick to a specific version of the data and it is not required to change after structure is changed in the parent.
  • Latest Mendix  version supports CRUD operations in DataHub, which helps to maintain a single source of truth.

2.4.2.3  Considerations

• There will be some rework when DataHub data version is changed. 

The post How Mendix can work with your Existing Database appeared first on Indium.

What is Gen AI and how does it work?

Certain types of Gen AI can generate code, write documentation, and even propose creative solutions based on your inputs. It leverages powerful NLP (Natural Language Processing) models trained on vast data to understand your intent and produce human-quality outputs.

GitHub Copilot, OpenAI CodeX, Microsoft Bonsai, and DeepCode are some of the Gen AI tools that propose relevant snippets, functions, or even entire lines of code. These tools understand natural language descriptions, adapt to your coding style, and can generate solutions for various languages and tasks. They can automatically generate unit tests, fix common bugs, and suggest refactoring improvements based on best practices.

However, these models are still under development, and their generated code might require human review and adjustments.

Key SDLC areas that Gen AI makes an impact

  1. Create intelligent workflows 

• Automating repetitive tasks: Gen AI can generate boilerplate code, unit tests, and API definitions, freeing your developers to focus on the core logic and complex algorithms.
• Intelligent code completion: Say goodbye to endless lines of manual coding. Gen AI can suggest relevant code snippets and functions based on context, significantly speeding up development.
• Rapid prototyping: Generative AI can generate interactive prototypes based on your descriptions or code, accelerating feedback loops and ensuring you’re on the right track early on.

2. Boost software quality 

• Bug-free code: Generative AI can analyze code and identify potential bugs and vulnerabilities, proactively mitigating issues before they become costly problems.
• Simplify testing: Generate diverse and comprehensive test cases with the help of AI, ensuring your software is robust and handles edge cases effectively.
• Security enhanced: Generative AI can identify and suggest solutions for potential security weaknesses, keeping your software safe and secure.

3. Spot code faults 

• Pattern detection: Like a seasoned code reviewer, AI can scan vast repositories, analyzing syntax, structure, and logic. It identifies patterns associated with common coding pitfalls, flagging potential issues before they become bugs.
• Bug prediction: Machine learning models trained on mountains of code learn the significant signs of trouble. They analyze your code, compare it to known bug patterns, and highlight areas that need attention.
• Anomaly detection: AI constantly monitors your code execution. It detects deviations from expected behavior and identifies suspicious code paths, helping you strip off potential bugs in the early stage.
• Learn from the past: AI taps into a treasure trove of knowledge—bug repositories and best practices databases. It learns from past mistakes and suggests solutions to similar issues in your code, preventing you from reinventing the wheel.
• Seamless integration: Gen AI can seamlessly integrate into your development environment and provide real-time feedback and alerts during coding, helping you catch errors on the fly.

4. Simplify testing

• Automated test case generation: Gen AI can create diverse test cases covering various scenarios, edge cases, and potential bugs, ensuring comprehensive testing.
• Improved test coverage: Identify areas where testing might be lacking and generate additional test cases to achieve thorough coverage.
• Early bug detection: Train Gen AI on your codebase to identify potential bugs and vulnerabilities before they cause problems in production.

5. Automate documentation 

• Auto-generated documentation: Manually churning out user guides and technical specs is time-consuming. Generative AI can analyze your code and user data to create accurate and up-to-date documentation, saving valuable time and resources.
• Consistent and accurate information: Eliminate inconsistencies and outdated documentation with AI-powered real-time updates that reflect your code changes.

How Gen AI transforms your SDLC?

Gen AI isn’t just about automation; it’s about augmentation. Imagine a tool that can:

• Increase speed: Eliminate repetitive tasks and streamline workflows, leading to faster development cycles and quicker time to market.
• Enhance quality: Gen AI can identify potential bugs, suggest optimizations, and write comprehensive tests, resulting in more robust and reliable software.
• Spark innovation: Explore new possibilities with AI-generated ideas and prototypes, pushing the boundaries of what your software can achieve.
• Improve collaboration: Break down knowledge silos by automatically summarizing documentation and generating clear communication materials.
• Empower developers: Shifting the focus from repetitive tasks to creative problem-solving and higher-level thinking fosters a more engaged and productive team.

The true power of Gen AI lies in its adaptability. By suggesting correct syntax and best practices and even fixing common bugs, these tools help prevent errors and improve code quality.

AI-powered coding is the future of software development—why?

The software development landscape is poised for a seismic shift. Generative AI promises to transform the way we code. This cutting-edge technology isn’t just impressive; it’s rapidly evolving, and it holds the key to unlocking next-generation software developers who are:

Extremely efficient: Imagine developers working twice as fast, churning out high-quality code with the help of AI. Generative AI can handle repetitive tasks like boilerplate code generation, freeing developers to focus on complex problem-solving and innovation.

Quality champions: With AI-powered tools, developers can quickly identify and fix bugs. Imagine your code being scanned in real-time, with potential issues highlighted before they become nightmares. It’s like having a built-in quality assurance team working tirelessly to ensure your software is clean.

Cost-conscious: Time is money, and Generative AI saves you both. Automating repetitive tasks and accelerating development brings your software to market faster and at a fraction of the cost.

Why Indium for implementing Gen AI in SDLC?

As an innovation-driven company, Indium is at the forefront of this exciting revolution. We understand the immense potential of Generative AI and are actively exploring its responsible implementation in the SDLC. Our dedicated AI experts are ready to unlock this technology’s power while addressing any security and compliance concerns you may have.

We can help transform your ideas into tangible results faster and better than ever before. Let’s work together to create the future of software development!

Finally

Gen AI is still evolving, but its potential for the SDLC is immense. By embracing this technology, you can unlock faster development cycles, high-quality software, and a more innovative future for your projects. So, step into the future and let Gen AI be your partner in transforming your SDLC!

The post How can Gen AI accelerate and transform your SDLC? appeared first on Indium.