Best Practices for Testing and Debugging LLM Workflows

"𝐖𝐡𝐲 𝐢𝐬 𝐦𝐲 𝐋𝐋𝐌 𝐠𝐢𝐯𝐢𝐧𝐠 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐚𝐧𝐬𝐰𝐞𝐫𝐬 𝐭𝐨 𝐭𝐡𝐞 𝐬𝐚𝐦𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧?"  If you have asked this in the last month, here is your Debugging Playbook. Most teams treat inconsistent LLM outputs as a Model Problem.  It is almost never the Model.  It is your System Architecture exposing variability you did not know existed. After debugging 40+ production AI systems, I have developed a 6-Step Framework that isolates the real culprit: Step 1: Confirm the Inconsistency Is Real • Compare responses across identical prompts • Control temperature, top-p, and randomness • Check prompt versions and hidden changes • Goal: Rule out noise before debugging the system Step 2: Break the Output into System Drivers • Decompose your response pipeline into components • Prompt structure, retrieved context (RAG), tool calls, model version, system instructions • Use a "dropped metric" approach to test each driver independently • Goal: Identify where variability can be introduced Step 3: Analyze Variability per Driver • Inspect each driver independently for instability • Does retrieval return different chunks? Are tool outputs non-deterministic? Are prompts dynamically constructed? • Test drivers across same period vs previous period • Goal: Isolate the component causing divergence Step 4: Segment by Execution Conditions • Slice outputs by environment or context • User input variants, model updates/routing, time-based data changes, token limits or truncation • Look for patterns in when inconsistency spikes • Goal: Find conditions where inconsistency spikes Step 5: Compare Stable vs Unstable Runs • Contrast successful outputs with failing ones • Same prompt/different output, same context/different reasoning, same goal/different execution • Surface the exact difference that matters • Goal: Surface the exact difference that matters Step 6: Form and Test Hypotheses • Turn findings into testable explanations • Hypothesis: retrieval drift, prompt ambiguity, tool response variance • Move from suspicion to proof • Goal: Move from suspicion to proof The pattern I see repeatedly: Teams jump straight to "let's try a different model" or "let's add more examples." But inconsistent outputs are rarely a model issue-they are usually a system issue. • Your retrieval is pulling different documents.  • Your tool is returning non-deterministic results.  • Your prompt is being constructed differently based on context length. The 6-step framework forces you to treat LLM systems like the distributed systems they actually are. Which step do most teams skip? Step 1. They assume inconsistency without proving it. Control your variables first. ♻️ Repost this to help your network get started ➕ Follow Anurag(Anu) Karuparti for more PS: If you found this valuable, join my weekly newsletter where I document the real-world journey of AI transformation. ✉️ Free subscription: https://lnkd.in/exc4upeq #GenAI #AIAgents

If you’ve ever shipped a GenAI model to production, you already know the real interview isn’t about transformers, it’s about everything that breaks the moment real users touch your system. 1) How would you evaluate an LLM powering a Q&A system? Approach: Don’t talk about accuracy alone. Break it down into: ✅ Functional metrics: exact match, F1, BLEU, ROUGE depending on task. ✅ Safety metrics: hallucination rate, refusal rate, PII leakage. ✅ User-facing metrics: latency, token cost, answer completeness. ✅ Human evaluation: rubric-based scoring from SMEs when answers aren’t deterministic. ✅ A/B tests: compare model variants on real user flows. 2) How do you handle hallucinations in production? Approach: ✅ Show you understand layered mitigation: ✅ Retrieval first (RAG) to ground the model. ✅ Constrain the prompt: citations, “answer only from provided context,” JSON schemas. ✅ Post-generation validation like fact-checking rules or context-overlap checks. ✅ Fall-back behaviors when confidence is low: ask for clarification, return source snippets, route to human. 3) You’re asked to improve retrieval quality in a RAG pipeline. What do you check first? Approach: Walk through a debugging flow: ✅ Check document chunking (size, overlap, boundaries). ✅ Evaluate embedding model suitability for domain. ✅ Inspect vector store configuration (HNSW params, top_k). ✅ Run retrieval diagnostics: is the top_k relevant to the question? ✅ Add metadata filters or rerankers (cross-encoder, ColBERT-style scoring). 4) How do you monitor a GenAI system after deployment? Approach: ✅ Make it clear that monitoring isn’t optional. ✅ Latency and cost per request. ✅ Token distribution shifts (prompt bloat). ✅ Hallucination drift from user conversations. ✅ Guardrail violations and safety triggers. ✅ Retrieval hit rate and query types. ✅ Feedback loops from thumbs up/down or human review. 5) How do you decide between fine-tuning and using RAG? Approach: ✅ Use a decision tree mentality: ✅ If the issue is knowledge freshness, go with RAG. ✅ If the issue is formatting/style, go with fine-tuning. ✅ If the model needs domain reasoning, consider fine-tuning or LoRA. ✅ If the data is large and structured, use RAG + reranking before touching training. Most interviews test what you know. GenAI interviews test what you’ve survived. Follow Sneha Vijaykumar for more... 😊 #genai #datascience #rag #production #interview #questions #careergrowth #prep

LLM systems don’t fail silently. They fail invisibly. No trace, no metrics, no alerts - just wrong answers and confused users. That’s why we architected a complete observability pipeline in the Second Brain AI Assistant course. Powered by Opik from Comet, it covers two key layers: 𝟭. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 → Tracks full prompt traces (inputs, outputs, system prompts, latencies) → Visualizes chain execution flows and step-level timing → Captures metadata like model IDs, retrieval config, prompt templates, token count, and costs Latency metrics like: Time to First Token (TTFT) Tokens per Second (TPS) Total response time ...are logged and analyzed across stages (pre-gen, gen, post-gen). So when your agent misbehaves, you can see exactly where and why. 𝟮. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 → Runs automated tests on the agent’s responses → Uses LLM judges + custom heuristics (hallucination, relevance, structure) → Works offline (during dev) and post-deployment (on real prod samples) → Fully CI/CD-ready with performance alerts and eval dashboards It’s like integration testing, but for your RAG + agent stack. The best part? → You can compare multiple versions side-by-side → Run scheduled eval jobs on live data → Catch quality regressions before your users do This is Lesson 6 of the course (and it might be the most important one). Because if your system can’t measure itself, it can’t improve. 🔗 Full breakdown here: https://lnkd.in/dA465E_J

Evaluating LLMs is hard. Evaluating agents is even harder. This is one of the most common challenges I see when teams move from using LLMs in isolation to deploying agents that act over time, use tools, interact with APIs, and coordinate across roles. These systems make a series of decisions, not just a single prediction. As a result, success or failure depends on more than whether the final answer is correct. Despite this, many teams still rely on basic task success metrics or manual reviews. Some build internal evaluation dashboards, but most of these efforts are narrowly scoped and miss the bigger picture. Observability tools exist, but they are not enough on their own. Google’s ADK telemetry provides traces of tool use and reasoning chains. LangSmith gives structured logging for LangChain-based workflows. Frameworks like CrewAI, AutoGen, and OpenAgents expose role-specific actions and memory updates. These are helpful for debugging, but they do not tell you how well the agent performed across dimensions like coordination, learning, or adaptability. Two recent research directions offer much-needed structure. One proposes breaking down agent evaluation into behavioral components like plan quality, adaptability, and inter-agent coordination. Another argues for longitudinal tracking, focusing on how agents evolve over time, whether they drift or stabilize, and whether they generalize or forget. If you are evaluating agents today, here are the most important criteria to measure: • 𝗧𝗮𝘀𝗸 𝘀𝘂𝗰𝗰𝗲𝘀𝘀: Did the agent complete the task, and was the outcome verifiable? • 𝗣𝗹𝗮𝗻 𝗾𝘂𝗮𝗹𝗶𝘁𝘆: Was the initial strategy reasonable and efficient? • 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻: Did the agent handle tool failures, retry intelligently, or escalate when needed? • 𝗠𝗲𝗺𝗼𝗿𝘆 𝘂𝘀𝗮𝗴𝗲: Was memory referenced meaningfully, or ignored? • 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 (𝗳𝗼𝗿 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀): Did agents delegate, share information, and avoid redundancy? • 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝘃𝗲𝗿 𝘁𝗶𝗺𝗲: Did behavior remain consistent across runs or drift unpredictably? For adaptive agents or those in production, this becomes even more critical. Evaluation systems should be time-aware, tracking changes in behavior, error rates, and success patterns over time. Static accuracy alone will not explain why an agent performs well one day and fails the next. Structured evaluation is not just about dashboards. It is the foundation for improving agent design. Without clear signals, you cannot diagnose whether failure came from the LLM, the plan, the tool, or the orchestration logic. If your agents are planning, adapting, or coordinating across steps or roles, now is the time to move past simple correctness checks and build a robust, multi-dimensional evaluation framework. It is the only way to scale intelligent behavior with confidence.

"Function calling isn’t working." "My Search tool is broken." "The agent isn't doing what I expect with BigQuery." Sound familiar? When a tool fails in an AI agent, the instinct is often to blame the framework 😁 And while we love (!) the feedback, as I get into the weeds with customers, we often find the issue hiding somewhere else. So it becomes important to start seeing the agent and its tools as a layer cake and apply classic software engineering discipline: isolate the failure by debugging layer by layer. Here’s the 4-layer framework for debugging tool-use with agents, and how to use adk web to do it: 1️⃣ The Tool Layer: Does your tool's code work in isolation? Before you even look at a trace, run your function with a hardcoded input. If it fails here, it's a bug in your tool's logic. 2️⃣ The Model Layer: Is the LLM generating the correct intent? This is where traces are invaluable. In adk web, look at the trace for the step right before the tool call. You can see the exact prompt sent to the model and the raw LLM output. Is the model choosing the right tool? Are the parameters plausible? If not, the issue is your prompt or tool description. 3️⃣ The Connection Layer: This is where the model's request meets your code. Is there a mismatch? Use adk web to check the exact arguments the LLM tried to pass to your function. Are the parameter names correct? Is a number being passed as a string? The trace makes it obvious if the LLM's understanding doesn't match your function's signature. 4️⃣ The Framework Layer: If the first three layers look good, now we look at the orchestration. How did the agent handle the tool's output? Use adk web to check the full trace is the story of your agent's execution. You can see the data returned by the tool and the subsequent LLM call where the agent decides what to do next. This is where you'll spot issues in your agent's logic flow. This methodical approach, powered by observability tools like traces, turns a vague "my agent is broken" into a more precise diagnosis. How do you debug your agents tool-use? Comment below if a deep dive into any of these area would be useful! #AI #Agents #Gemini #DeveloperTools #FunctionCalling #Debugging #Observability

If you’re building with LLMs, these are 10 toolkits I highly recommend getting familiar with 👇 Whether you’re an engineer, researcher, PM, or infra lead, these tools are shaping how GenAI systems get built, debugged, fine-tuned, and scaled today. They form the core of production-grade AI, across RAG, agents, multimodal, evaluation, and more. → AI-Native IDEs (Cursor, JetBrains Junie, Copilot X) Modern IDEs now embed LLMs to accelerate coding, testing, and debugging. They go beyond autocomplete, understanding repo structure, generating unit tests, and optimizing workflows. → Multi-Agent Frameworks (CrewAI, AutoGen, LangGraph) Useful when one model isn’t enough. These frameworks let you build role-based agents (e.g. planner, retriever, coder) that collaborate and coordinate across complex tasks. → Inference Engines (Fireworks AI, vLLM, TGI) Designed for high-throughput, low-latency LLM serving. They handle open models, fine-tuned variants, and multimodal inputs, essential for scaling to production. → Data Frameworks for RAG (LlamaIndex, Haystack, RAGflow) Builds the bridge between your data and the LLM. These frameworks handle parsing, chunking, retrieval, and indexing to ground model outputs in enterprise knowledge. → Vector Databases (Pinecone, Weaviate, Qdrant, Chroma) Backbone of semantic search. They store embeddings and power retrieval in RAG, recommendations, and memory systems using fast nearest-neighbor algorithms. → Evaluation & Benchmarking (Fireworks AI Eval Protocol, Ragas, TruLens) Lets you test for accuracy, hallucinations, regressions, and preference alignment. Core to validating model behavior across prompts, versions, or fine-tuning runs. → Memory Systems (MEM-0, LangChain Memory, Milvus Hybrid) Enables agents to retain past interactions. Useful for building persistent assistants, session-aware tools, and long-term personalized workflows. → Agent Observability (LangSmith, HoneyHive, Arize AI Phoenix) Debugging LLM chains is non-trivial. These tools surface traces, logs, and step-by-step reasoning so you can inspect and iterate with confidence. → Fine-Tuning & Reward Stacks (PEFT, LoRA, Fireworks AI RLHF/RLVR) Supports adapting base models efficiently or aligning behavior using reward models. Great for domain tuning, personalization, and safety alignment. → Multimodal Toolkits (CLIP, BLIP-2, Florence-2, GPT-4o APIs) Text is just one modality. These toolkits let you build agents that understand images, audio, and video, enabling richer input/output capabilities. If you're deep in AI infra or systems, print this out, build a test project around each, and experiment with how they fit together. You’ll learn more in a weekend with these tools than from hours of reading docs. What’s one tool you’d add to this list? 👇 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI infrastructure insights, and subscribe to my newsletter for deeper technical breakdowns: 🔗 https://lnkd.in/dpBNr6Jg

Don't ask an LLM to do your evals. Instead, use it to accelerate them. LLMs can speed up parts of your eval workflow, but they can’t replace human judgment where your expertise is essential. Here are some areas where LLMs can help: 1. First-pass axial coding: After you’ve open coded 30–50 traces yourself, use an LLM to organize your raw failure notes into proposed groupings. This helps you quickly spot patterns, but always review and refine the clusters yourself. Note: If you aren’t familiar with axial and open coding, see this faq: https://lnkd.in/gpgDgjpz 2. Mapping annotations to failure modes: Once you’ve defined failure categories, you can ask an LLM to suggest which categories apply to each new trace (e.g., “Given this annotation: [open_annotation] and these failure modes: [list_of_failure_modes], which apply?”). 3. Suggesting prompt improvements: When you notice recurring problems, have the LLM propose concrete changes to your prompts. Review these suggestions before adopting any changes. 4. Analyzing annotation data: Use LLMs or AI-powered notebooks to find patterns in your labels, such as “reports of lag increase 3x during peak usage hours” or “slow response times are mostly reported from users on mobile devices.” However, you shouldn’t outsource these activities to an LLM: 1. Initial open coding: Always read through the raw traces yourself at the start. This is how you discover new types of failures, understand user pain points, and build intuition about your data. Never skip this or delegate it. 2. Validating failure taxonomies: LLM-generated groupings need your review. For example, an LLM might group both “app crashes after login” and “login takes too long” under a single “login issues” category, even though one is a stability problem and the other is a performance problem. Without your intervention, you’d miss that these issues require different fixes. 3. Ground truth labeling: For any data used for testing/validating LLM-as-Judge evaluators, hand-validate each label. LLMs can make mistakes that lead to unreliable benchmarks. 4. Root cause analysis: LLMs may point out obvious issues, but only human review will catch patterns like errors that occur in specific workflows or edge cases—such as bugs that happen only when users paste data from Excel. Start by examining data manually to understand what’s going wrong. Use LLMs to scale what you’ve learned, not to avoid looking at data. Read this and other eval tips here: https://lnkd.in/gfUWAjR3

Traditional CI/CD doesn’t work for agents. Here's something that does— Most of the testing systems we’ve developed over the years depend on the output being deterministic by nature. A + B = C But when it comes to agents, the same input could easily deliver two completely different outputs. A + B might equal C. But it might also equal Z. Or H. Or a pot roast. Who knows! In his recent post, Elor Arieli PhD Arieli shares how his team rethought the process of CI/CD from first principles to support the continuous reliability of Monte Carlo’s own Troubleshooting Agent—a tool that leverages hundreds of sub-agents to expedite root-cause analysis. According to Elor, his team evaluates three main categories— -- Semantic distance – How similar is the actual response to the expected output? Is the meaning intact but with different wording or is it substantially incorrect?  -- Groundedness – Did the agent retrieve the right context, and if so, did it use it correctly? -- Tool usage – Did the agent use the right tools in the right way? To uncover these metrics, his team uses a mix of LLM-as-judge evaluators that score responses on a scale of 0-1, along with cost-efficient deterministic tests to validate things like whether an output was delivered in the right format or that the guardrails were called as intended. According to Elor, his team has identified 5 best practices that make this testing program work— 1. Leave room for soft failures — When using LLM-as-judge evaluations, a little failure is okay. But multiple little failures can be a big problem. Know what those thresholds are and build failsafes to handle them. 2 Automate re-evaluations — Sometimes it isn’t the response that fails, it’s the test. An automatic retry mitigates the impact of false negatives. 3. Ask for explanations — It’s not enough to know when something fails. You need to know why. Don’t just ask for a score, ask for an explanation. 4. Evaluate your evaluators — Like we said, tests can fail. Elor and team run tests multiple times. If the delta is too large, the test is revised or removed. 5. Use localized tests and conservative triggers — Evaluating agents is expensive; sometimes 10x the cost of running the agent itself. So, be deliberate about when and how you test. Instead of doing an entire run to test outputs, Elor's team opts for localized tests, supplying a portion of the programs output proactively. And these tests are only triggered when a PR modifies specific components of the agent, with the stated goal of less than 1:1 ratio of testing to operation costs. If you're building evaluations and you have a moment, this one is worth a read! 

𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲’𝘀 𝗼𝗯𝘀𝗲𝘀𝘀𝗲𝗱 𝘄𝗶𝘁𝗵 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 𝘁𝗵𝗲𝗶𝗿 𝗟𝗟𝗠𝘀. But most teams aren’t even testing the default behavior properly. A team we spoke to spent 6 weeks fine-tuning a model to reduce hallucinations in a customer support workflow. What they didn’t realize? The base model was already mostly fine. The hallucinations were triggered by edge-case phrasing—stuff their devs never thought to test for. What actually solved it? Not fine-tuning. Rigorous scenario-based testing with Ragmetrics. They fed real prompts, real tasks, real failure cases through our eval framework—and uncovered inconsistencies that only showed up under pressure. No more guessing. No more hallucinations at the worst time. Here’s the thing: 💡 You don’t need to fine-tune if you haven’t test-tuned first. Start with evaluation. Then optimize. If you’re building with LLMs and want to make sure your model actually behaves when it counts, happy to share what we’ve seen work. Just drop a comment or DM—I’ll send over the playbook.

Struggling with poor results from your RAG (Retrieval-Augmented Generation) system? Before you blame the model, take a closer look at the entire retrieval pipeline. This guide outlines 6 common failure points in RAG workflows—and what you can do to fix each one. 1. Missing Content Your system can't retrieve relevant answers if the data simply doesn’t exist in your database. 2. Missing Top Ranked Documents Relevant docs might exist but rank too low in retrieval results to be useful. 3. Not in Context (Chunking/Truncation Issues) The right info is retrieved, but never reaches the LLM due to poor chunking or truncation. 4. Not Extracted The LLM sees the right answer but fails to extract it due to noise or lack of prompt clarity. 5. Wrong Output Format LLM provides an answer, but it’s unstructured, unreadable, or not in the expected schema. 6. Incorrect Specificity The output is too vague or overly detailed, lacking the right balance. ✅ Use this checklist to debug your pipeline—from retrieval quality to formatting—to get the most out of your LLM-powered applications.