Managing Project Quality Assurance

Last week, I described four design patterns for AI agentic workflows that I believe will drive significant progress: Reflection, Tool use, Planning and Multi-agent collaboration. Instead of having an LLM generate its final output directly, an agentic workflow prompts the LLM multiple times, giving it opportunities to build step by step to higher-quality output. Here, I'd like to discuss Reflection. It's relatively quick to implement, and I've seen it lead to surprising performance gains. You may have had the experience of prompting ChatGPT/Claude/Gemini, receiving unsatisfactory output, delivering critical feedback to help the LLM improve its response, and then getting a better response. What if you automate the step of delivering critical feedback, so the model automatically criticizes its own output and improves its response? This is the crux of Reflection. Take the task of asking an LLM to write code. We can prompt it to generate the desired code directly to carry out some task X. Then, we can prompt it to reflect on its own output, perhaps as follows: Here’s code intended for task X: [previously generated code] Check the code carefully for correctness, style, and efficiency, and give constructive criticism for how to improve it. Sometimes this causes the LLM to spot problems and come up with constructive suggestions. Next, we can prompt the LLM with context including (i) the previously generated code and (ii) the constructive feedback, and ask it to use the feedback to rewrite the code. This can lead to a better response. Repeating the criticism/rewrite process might yield further improvements. This self-reflection process allows the LLM to spot gaps and improve its output on a variety of tasks including producing code, writing text, and answering questions. And we can go beyond self-reflection by giving the LLM tools that help evaluate its output; for example, running its code through a few unit tests to check whether it generates correct results on test cases or searching the web to double-check text output. Then it can reflect on any errors it found and come up with ideas for improvement. Further, we can implement Reflection using a multi-agent framework. I've found it convenient to create two agents, one prompted to generate good outputs and the other prompted to give constructive criticism of the first agent's output. The resulting discussion between the two agents leads to improved responses. Reflection is a relatively basic type of agentic workflow, but I've been delighted by how much it improved my applications’ results. If you’re interested in learning more about reflection, I recommend: - Self-Refine: Iterative Refinement with Self-Feedback, by Madaan et al. (2023) - Reflexion: Language Agents with Verbal Reinforcement Learning, by Shinn et al. (2023) - CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing, by Gou et al. (2024) [Original text: https://lnkd.in/g4bTuWtU ]

🚗 Imagine this: You launch a new car model after years of effort. Production is smooth, the assembly line is world-class… but six months later, the headlines scream “Massive Recall.” Billions lost. Reputation damaged. All because of a design flaw that was locked in during the product development phase. Takao Sakai once said: 👉 “95% of Toyota’s profits are determined in the product development phase, not production.” And it’s true across industries: In aerospace, material choices made at the design table decide 80% of lifecycle costs. In electronics, overengineering features adds cost but not value. In manufacturing, late design changes cause delays that no production efficiency can recover. ⚡ The real challenge? Most companies pour their energy into fixing problems on the shop floor instead of preventing them during development. 💡 The smarter way: Apply Design for Manufacturability (DFM) & Concurrent Engineering. Run early simulations & prototypes to detect risks. Involve quality, supply chain, and production teams at the concept stage. Use Voice of Customer (VOC) to cut out features no one wants but everyone pays for. The truth is simple: ✅ Every mistake caught in design costs a fraction of fixing it in production. ✅ Every smart decision in development compounds into long-term profit. 🔑 What’s one thing your team does during product development that safeguards future profitability? 👇 Share your experience—it might spark ideas for someone else! #Lean #ProductDevelopment #DesignThinking #Innovation #BusinessExcellence #Quality #TQM

As a client project manager, I consistently found that offshore software development teams from major providers like Infosys, Accenture, IBM, and others delivered software that failed 1/3rd of our UAT tests after the provider's independent dedicated QA teams passed it. And when we got a fix back, it failed at the same rate, meaning some features cycled through Dev/QA/UAT ten times before they worked. I got to know some of the onshore technical leaders from these companies well enough for them to tell me confidentially that we were getting such poor quality because the offshore teams were full of junior developers who didn't know what they were doing and didn't use any modern software engineering practices like Test Driven Development. And their dedicated QA teams couldn't prevent these quality issues because they were full of junior testers who didn't know what they were doing, didn't automate tests and were ordered to test and pass everything quickly to avoid falling behind schedule. So, poor quality development and QA practices were built into the system development process, and independent QA teams didn't fix it. Independent dedicated QA teams are an outdated and costly approach to quality. It's like a car factory that consistently produces defect-ridden vehicles only to disassemble and fix them later. Instead of testing and fixing features at the end, we should build quality into the process from the start. Modern engineering teams do this by working in cross-functional teams. Teams that use test-driven development approaches to define testable requirements and continuously review, test, and integrate their work. This allows them to catch and address issues early, resulting in faster, more efficient, and higher-quality development. In modern engineering teams, QA specialists are quality champions. Their expertise strengthens the team’s ability to build robust systems, ensuring quality is integral to how the product is built from the outset. The old model, where testing is done after development, belongs in the past. Today, quality is everyone’s responsibility—not through role dilution but through shared accountability, collaboration, and modern engineering practices.

Building LLM apps? Learn how to test them effectively and avoid common mistakes with this ultimate guide from LangChain! 🚀 This comprehensive document highlights: 1️⃣ Why testing matters: Tackling challenges like non-determinism, hallucinated outputs, and performance inconsistencies. 2️⃣ The three stages of the development cycle: 💥 Design: Incorporating self-corrective mechanisms for error handling (e.g., RAG systems and code generation). 💥Pre-Production: Building datasets, defining evaluation criteria, regression testing, and using advanced techniques like pairwise evaluation. 💥Post-Production: Monitoring performance, collecting feedback, and bootstrapping to improve future versions. 3️⃣ Self-corrective RAG applications: Using error handling flows to mitigate hallucinations and improve response relevance. 4️⃣ LLM-as-Judge: Automating evaluations while reducing human effort. 5️⃣ Real-time online evaluation: Ensuring your LLM stays robust in live environments. This guide offers actionable strategies for designing, testing, and monitoring your LLM applications efficiently. Check it out and level up your AI development process! 🔗📘 ------------ Add your thoughts in the comments below—I’d love to hear your perspective! Sarveshwaran Rajagopal #AI #LLM #LangChain #Testing #AIApplications

How do the best product teams ship better and faster without things breaking? They embed quality into every step of the development process. Here’s how you can do the same: — 𝗙𝗶𝘃𝗲 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗖𝗵𝗲𝗰𝗸𝗽𝗼𝗶𝗻𝘁𝘀 𝗢𝗡𝗘 - 𝗕𝗲𝗳𝗼𝗿𝗲 𝗙𝗶𝗻𝗮𝗹𝗶𝘇𝗶𝗻𝗴 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀 → When: Right after you’ve done your research and before locking down specs. → Why it matters: This is your chance to clarify any confusion, resolve technical risks, and ensure alignment before things snowball. → Win: Fewer mid-sprint surprises and last-minute changes. — 𝗧𝗪𝗢 - 𝗗𝘂𝗿𝗶𝗻𝗴 𝗘𝗮𝗿𝗹𝘆 𝗗𝗲𝘀𝗶𝗴𝗻 𝗥𝗲𝘃𝗶𝗲𝘄𝘀 → When: As soon as wireframes or low-fidelity prototypes start taking shape. → Why it matters: You can catch complexity, edge cases, and UX blind spots before they become costly problems. → Win: Fewer redesigns, smoother testing, and faster handoffs to development. — 𝗧𝗛𝗥𝗘𝗘 - 𝗕𝗲𝗳𝗼𝗿𝗲 𝗕𝗲𝗴𝗶𝗻𝗻𝗶𝗻𝗴 𝗙𝘂𝗹𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 → When: After design has stabilized but before full-scale coding kicks off. → Why it matters: Confirm the technical feasibility, refine strategies, and estimate accurately so nothing derails you down the line. → Win: Fewer slowdowns and more predictable delivery timelines. — 𝗙𝗢𝗨𝗥 - 𝗣𝗿𝗲-𝗟𝗮𝘂𝗻𝗰𝗵 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 & 𝗗𝗿𝘆 𝗥𝘂𝗻𝘀 → When: Right before launch, after QA rounds and beta testing are complete. → Why it matters: This is your dress rehearsal—simulate real-world conditions, verify stability, and make sure everything works under pressure. → Win: A smooth, stable launch with fewer emergency fixes. — 𝗙𝗜𝗩𝗘 - 𝗣𝗼𝘀𝘁-𝗟𝗮𝘂𝗻𝗰𝗵 𝗘𝗮𝗿𝗹𝘆 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 → When: Immediately after go-live, during the first few hours and days. → Why it matters: Keep a close watch on user behavior, unexpected errors, and drop-offs so you can act fast. → Win: Faster issue resolution, stronger retention, and happier users. — The big lesson? Most problems don’t appear out of nowhere. They’re born in earlier stages and snowball when left unchecked. By building these 5 checkpoints into your product process, you: → Catch issues before users notice. → Save time, money, and your team’s sanity. → Deliver a product you’re proud of. — Deep dive is available in the comments. 👇

Working with multiple LLM providers, prompt engineering, and complex data flows requires thoughtful organization. A proper structure helps teams: - Maintain clean separation between configuration and code - Implement consistent error handling and rate limiting - Enable rapid experimentation while preserving reproducibility - Facilitate collaboration across ML engineers and developers The modular approach shown here separates model clients, prompt engineering, utils, and handlers while maintaining a coherent flow. This organization has saved many people countless hours in debugging and onboarding. Key Components That Drive Success Beyond folders, the real innovation lies in how components interact: - Centralized configuration through YAML - Dedicated prompt engineering module with templating and few-shot capabilities - Properly sandboxed model clients with standardized interfaces - Comprehensive caching, logging, and rate limiting Whether you're building RAG applications, fine-tuning foundation models, or creating agent-based systems, this structure provides a solid foundation to build upon. What project structure approaches have you found effective for your generative AI projects? I'd love to hear your experiences.

Did you know that weak measurement and verification systems can undermine the credibility of entire sustainability and climate programs? Recent analysis by Senken of more than 2,300 carbon projects found that in some categories, fewer than 16% of issued carbon credits corresponded to real emission reductions, highlighting the risks of inadequate monitoring and verification systems. At the same time, global climate finance and carbon markets depend on rigorous Measurement, Reporting, and Verification (MRV) processes; because one verified carbon credit represents one tonne of greenhouse gas emissions reduced or removed, a unit that governments, investors, and institutions rely on to track real progress. These numbers reinforce a simple but critical lesson: credibility in sustainability is built on systems, not promises. In practice, this means investing in robust monitoring frameworks, conducting independent compliance audits, and ensuring that data can withstand scrutiny from regulators, financiers, and stakeholders. Organizations that prioritize these systems are not only better prepared for evolving disclosure requirements, they are also better positioned to attract investment, manage risk, and deliver measurable impact. As sustainability expectations continue to rise globally, the institutions that will lead are those that understand that accountability is not an administrative requirement; it is a strategic asset. Because in sustainability and climate action, what gets measured, verified, and audited is what ultimately builds trust and delivers lasting results.

Small variations in prompts can lead to very different LLM responses. Research that measures LLM prompt sensitivity uncovers what matters, and the strategies to get the best outcomes. A new framework for prompt sensitivity, ProSA, shows that response robustness increases with factors including higher model confidence, few-shot examples, and larger model size. Some strategies you should consider given these findings: 💡 Understand Prompt Sensitivity and Test Variability: LLMs can produce different responses with minor rephrasings of the same prompt. Testing multiple prompt versions is essential, as even small wording adjustments can significantly impact the outcome. Organizations may benefit from creating a library of proven prompts, noting which styles perform best for different types of queries. 🧩 Integrate Few-Shot Examples for Consistency: Including few-shot examples (demonstrative samples within prompts) enhances the stability of responses, especially in larger models. For complex or high-priority tasks, adding a few-shot structure can reduce prompt sensitivity. Standardizing few-shot examples in key prompts across the organization helps ensure consistent output. 🧠 Match Prompt Style to Task Complexity: Different tasks benefit from different prompt strategies. Knowledge-based tasks like basic Q&A are generally less sensitive to prompt variations than complex, reasoning-heavy tasks, such as coding or creative requests. For these complex tasks, using structured, example-rich prompts can improve response reliability. 📈 Use Decoding Confidence as a Quality Check: High decoding confidence—the model’s level of certainty in its responses—indicates robustness against prompt variations. Organizations can track confidence scores to flag low-confidence responses and identify prompts that might need adjustment, enhancing the overall quality of outputs. 📜 Standardize Prompt Templates for Reliability: Simple, standardized templates reduce prompt sensitivity across users and tasks. For frequent or critical applications, well-designed, straightforward prompt templates minimize variability in responses. Organizations should consider a “best-practices” prompt set that can be shared across teams to ensure reliable outcomes. 🔄 Regularly Review and Optimize Prompts: As LLMs evolve, so may prompt performance. Routine prompt evaluations help organizations adapt to model changes and maintain high-quality, reliable responses over time. Regularly revisiting and refining key prompts ensures they stay aligned with the latest LLM behavior. Link to paper in comments.

In the last few months, I have explored LLM-based code generation, comparing Zero-Shot to multiple types of Agentic approaches. The approach you choose can make all the difference in the quality of the generated code. Zero-Shot vs. Agentic Approaches: What's the Difference? ⭐ Zero-Shot Code Generation is straightforward: you provide a prompt, and the LLM generates code in a single pass. This can be useful for simple tasks but often results in basic code that may miss nuances, optimizations, or specific requirements. ⭐ Agentic Approach takes it further by leveraging LLMs in an iterative loop. Here, different agents are tasked with improving the code based on specific guidelines—like performance optimization, consistency, and error handling—ensuring a higher-quality, more robust output. Let’s look at a quick Zero-Shot example, a basic file management function. Below is a simple function that appends text to a file: def append_to_file(file_path, text_to_append): try: with open(file_path, 'a') as file: file.write(text_to_append + '\n') print("Text successfully appended to the file.") except Exception as e: print(f"An error occurred: {e}") This is an OK start, but it’s basic—it lacks validation, proper error handling, thread safety, and consistency across different use cases. Using an agentic approach, we have a Developer Lead Agent that coordinates a team of agents: The Developer Agent generates code, passes it to a Code Review Agent that checks for potential issues or missing best practices, and coordinates improvements with a Performance Agent to optimize it for speed. At the same time, a Security Agent ensures it’s safe from vulnerabilities. Finally, a Team Standards Agent can refine it to adhere to team standards. This process can be iterated any number of times until the Code Review Agent has no further suggestions. The resulting code will evolve to handle multiple threads, manage file locks across processes, batch writes to reduce I/O, and align with coding standards. Through this agentic process, we move from basic functionality to a more sophisticated, production-ready solution. An agentic approach reflects how we can harness the power of LLMs iteratively, bringing human-like collaboration and review processes to code generation. It’s not just about writing code; it's about continuously improving it to meet evolving requirements, ensuring consistency, quality, and performance. How are you using LLMs in your development workflows? Let's discuss!