Practical LLM Testing Skills for AI Engineers

You’re in an AI Engineer interview. The interviewer asks: “Your summarization agent works perfectly on your local. Now deploy it to production, what do you do next?” Here’s how I’d think about it 👇 A local demo proves your idea works. Production proves your system survives. 1. Start with reproducibility I’d containerize the app using Docker. If it doesn’t run the same way everywhere, nothing else matters. 2. Rethink the model strategy In local setups, we overuse powerful models. In production, that’s expensive and slow. So I’d: route simple tasks to smaller models reserve larger models for complex cases introduce async or batching where possible This is where you balance performance with cost. 3. Handle real-world inputs Users won’t give clean text like your test data. So I’d add: chunking for long documents preprocessing pipelines guardrails for unexpected inputs 4. Add observability (non-negotiable) Adding visibility into: prompts and responses latency token usage failure cases Without this, debugging becomes guesswork. 5. Build an evaluation system Introducing benchmark datasets LLM-based or human evaluation metrics like faithfulness and summary quality And this runs continuously, not once. 6. Improve consistency and reliability LLMs are inherently non-deterministic. So I’d: version prompts control temperature add retries and fallback models cache frequent outputs Consistency builds trust. 7. Optimize for cost This is where most systems break at scale. I’d: cache responses limit token usage dynamically choose models reduce unnecessary context 8. Close the loop with feedback Capture real user interactions. Find where the system fails. Continuously improve. If you’re preparing for AI/ML interviews, this is the level of thinking that sets you apart. #ai #llm #datascience #aiengineering #aiinterviews #interview Follow Sneha Vijaykumar for more...😊

Building LLM apps? Learn how to test them effectively and avoid common mistakes with this ultimate guide from LangChain! 🚀 This comprehensive document highlights: 1��⃣ 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

Your LLM app isn't broken because of the model. It's broken because you never measured it. AI Evals!! Most teams do the same thing: → Build it → Test it on 5 examples → Demo goes perfectly → Ship it → Pray Then 3 weeks in, a user screenshots your chatbot confidently hallucinating your own product pricing. Here's the eval stack that actually works: 1/ Golden dataset first. Even 20 hand-crafted examples with validated answers are enough to start. Quality over quantity. This is your source of truth. 2/ Two types of evaluators — both are required. LLM-as-judge for subjective signals (hallucination, relevance, tone). Code-based eval for structural checks (did the JSON parse? is the number in range?). One without the other is incomplete. 3/ Never use 1–10 scores. LLMs can't score consistently at that granularity across runs. Use binary (correct/incorrect) or multi-class (relevant/partially relevant/irrelevant). You can average those. You can't trust a score of 7.2. 4/ Wire evals to CI/CD. Every prompt change, model swap, or retrieval tweak runs against your golden dataset before it ships. This is your gate. LLM evaluations are your new unit tests. 5/ Add guardrails last, not first. Don't block everything. Over-indexing on guards kills user intent. Start with PII removal, jailbreak detection, and hallucination prevention. Add more when production tells you to. Your app can degrade with zero code changes. Model updates and input drift happen silently. Run your evals on a schedule, not just on deploys. Measure it. Or be surprised by it. What's your current eval setup? Drop it in the comments. Read the full blog and follow me Priyanka for more ↓ https://lnkd.in/gsjnbubY #LLMOps #AIEngineering #MachineLearning #GenerativeAI #MLOps #SoftwareEngineering #AIProductDevelopment #evals #aievals

Your LLM app feels buggy, but you can't pinpoint why. On Lenny Rachitsky's podcast, Shreya Shankar and I broke down the solution: a systematic AI evaluation workflow. Here is the workflow we teach thousands of engineers and PMs, including those at OpenAI, Google, Meta and others: 1. Open coding: Manually review traces and write notes on failure modes (e.g., hallucinations, poor handoffs, janky flows) 2. Axial coding: Use LLMs to cluster those notes into concrete, repeatable failure types 3. Prioritize with data: Do data analysis to understand which issues happen most and which are most severe. 4. Automated evaluators: Build code-based evals (e.g., JSON formatting, tool call correctness) or LLM-as-judge (e.g., “Did the agent fail to escalate when it should?”) 5. Run your evals in CI/CD and in production monitoring to catch regressions and discover issues. Many teams skip this. They ship prompts, see weird behavior, and guess at the root cause. That guesswork doesn’t scale. Evals make that guesswork go away. They turn requirements into executable specs, constantly validating whether your agent is behaving the way you expect. If you’d like to demystify the process of developing effective evals and learn techniques to improve your AI product, you can join our next Maven cohort on October 6: http://bit.ly/4pDmoiV

LLM applications are frustratingly difficult to test due to their probabilistic nature. However, testing is crucial for customer-facing applications to ensure the reliability of generated answers. So, how does one effectively test an LLM app? Enter Confident AI's DeepEval: a comprehensive open-source LLM evaluation framework with excellent developer experience. Key features of DeepEval: - Ease of use: Very similar to writing unit tests with pytest. - Comprehensive suite of metrics: 14+ research-backed metrics for relevancy, hallucination, etc., including label-less standard metrics, which can quantify your bot's performance even without labeled ground truth! All you need is input and output from the bot. See the list of metrics and required data in the image below! - Custom Metrics: Tailor your evaluation process by defining your custom metrics as your business requires. - Synthetic data generator: Create an evaluation dataset synthetically to bootstrap your tests My recommendations for LLM evaluation: - Use OpenAI GPT4 as the metric model as much as possible. - Test Dataset Generation: Use the DeepEval Synthesizer to generate a comprehensive set of realistic questions! Bulk Evaluation: If you are running multiple metrics on multiple questions, generate the responses once, store them in a pandas data frame, and calculate all the metrics in bulk with parallelization. - Quantify hallucination: I love the faithfulness metric, which indicates how much of the generated output is factually consistent with the context provided by the retriever in RAG! CI/CD: Run these tests automatically in your CI/CD pipeline to ensure every code change and prompt change doesn't break anything. - Guardrails: Some high-speed tests can be run on every API call in a post-processor before responding to the user. Leave the slower tests for CI/CD. 🌟 DeepEval GitHub: https://lnkd.in/g9VzqPqZ 🔗 DeepEval Bulk evaluation: https://lnkd.in/g8DQ9JAh Let me know in the comments if you have other ways to test LLM output systematically! Follow me for more tips on building successful ML and LLM products! Medium: https://lnkd.in/g2jAJn5 X: https://lnkd.in/g_JbKEkM #generativeai #llm #nlp #artificialintelligence #mlops #llmops

One of the first agents I built was extremely simple: It retrieved information from a vector store, formatted it as HTML, and emailed it to the user. It doesn't get simpler than this, and yet, this agent failed about 1% of the time. No error. No warning. It just returned garbage. Here is the harsh truth: Agents fail a lot. And they fail silently. All the time. You just can't trust an LLM to do the right thing every time. By now, I've built and deployed a couple of dozen agents, and here are some of the things that actually work: 1. Observability from day one. If you can't see what your agent is doing, you can't debug it, improve it, or trust it. Every agent should produce traces showing the full request flow, model interactions, token usage, and timing metadata. 2. Guardrails on inputs and outputs. Everything that goes into and comes out of an LLM should be checked by deterministic code. Even things that aren't likely to break will eventually break. 3. LLM-as-a-judge evaluation. You can build a simple judge using an LLM to automatically evaluate your agent's outputs. Label a dataset, write the evaluation prompt, and iterate until your judge catches most failures. 4. Error analysis. You can collect failure samples, categorize them, and diagnose the most frequent mistakes. 5. Context engineering. Often, agents fail because their context is noisy, overloaded, or irrelevant. Learning how to keep context relevant is huge. 6. Human feedback loops. Sometimes the best guardrail is a human in the loop, especially for high-stakes decisions. I'm covering all of these techniques in depth in my AI/ML Engineering cohort. If you want to build agents that actually work in production, this is the stuff that matters and makes a difference. Next cohort starts February 2. Join here: ml.school

QA Skills? Unsure how to transition them into high paying AI roles? Here are 5 GitHub projects every QA AI Eval Engineer should have: 🟢 01 → LLM Eval Framework A real end-to-end eval suite (promptfoo is a great tool for this) for a specific use case. Shows you can design test cases, write assertions, and measure model quality systematically. 🟢 02 → Red Teaming Suite Adversarial testing — prompt injections, jailbreaks, bias detection. 🟢 03 → Custom LLM Judge Build your own LLM-as-a-judge with a clear scoring rubric. This separates people who use eval tools from people who understand how eval scoring actually works. 🟢 04 → Regression Testing Pipeline CI/CD integrated evals via GitHub Actions that auto-fail when quality drops. Shows you think like a software engineer, not just a tester. 🟢 05 → Benchmark Comparison Compare models on a specific task with a structured methodology and a written analysis. Hiring managers want engineers who can communicate findings, not just run tests. ⁉️ Unsure how to do this? AI can walk you through any and all of these projects. 💰 I've been hiring QA Engineers for over a decade. The hiring landscape is changing. QA skills are still extremely valuable; even more so in the age of AI. Understanding and demonstrating how to use them in the AI context will keep you competitive. What other AI related portfolio projects have you worked on? #AIEval #QAEngineering #LLMTesting #MachineLearning #PromptEngineering #GitHubPortfolio #AIEngineering