Building LLM Test Suites for Startup Teams

Do you need to learn how to properly evaluate your agent? Here’s a step-by-step guide for how to do this, informed by best practices in recent research… (1) Define success. We need to first think about what it means for the agent to succeed. We should write clear and detailed criteria such as: - Outcome goals that verify aspects of the outcome (e.g., whether the expected database entries for the task were created). - Process goals that verify components of the transcript (e.g., whether certain tools were called). Recent agent benchmarks are heavily outcome-oriented, as outcome goals provide a reliable and objective mechanism for assessing the success of an agent. (2) Collect a small task set. Instead of curating a lot of data up front, we can start with a small number of tasks that we manually curate for evaluating the agent. As we use the agent and find new failure cases, we should record these issues and use them to add new tasks to our evaluation suite. Over time, we should continue collecting new—usually more difficult—tasks that challenge the agent. Legacy tasks can be maintained in a regression set. (3) Create useful tasks. We should create high-quality tasks that test important aspects of agent behavior in a reliable manner. Tasks should be clear enough that repeated evaluations yield consistent results. Ambiguous or noisy tasks complicate the evaluation process with unstable and misleading results that can obfuscate the actual performance of an agent. (4) Configure graders. We should begin with simple graders like deterministic checks (e.g., check if tools were called or if a final answer matches ground truth) because they are simple and easy to debug. For subjective criteria (e.g., code style) we need model-based graders (LLM-as-a-Judge) or human review. The human evaluation process should be calibrated, and we should monitor the level of agreement between LLM judges and human experts. (5) Build the evaluation harness. We must be able to execute the evaluation efficiently and repeatably. To do this, we can create an evaluation harness that: - Runs the agent in a realistic (but controlled) setup. - Collects the transcript, including tool calls and intermediate outputs. - Captures the final outcome. The agent should ideally use the same scaffold, tools, and environment that are used in production during the evaluation process. Each trial should start from a fresh environment to avoid any failures caused by shared state or evaluation infrastructure issues. (6) Inspect, iterate, and maintain the benchmark. Agent evaluations can become saturated quickly, so we should treat evaluation suites as living artifacts that continually improve in difficulty, diversity, and reliability. The best agent evaluations evolve continuously through new failure cases and ongoing maintenance.

Most teams pick an LLM based on vibes and benchmarks. Both will fail you in production. The 9-point LLM production checklist 1. P50 / P95 latency under real load Don't test cold. Simulate concurrent users. A model that's fast at 1 req/s often chokes at 50. Measure time-to-first-token separately — it dominates perceived speed. Target: P95 TTFT< 1.5s for chat, < 500ms for autocomplete 2. True cost per 1M tokens (input + output) Providers quote input prices. Your app is mostly output tokens. Model your actual input/output ratio — most apps run 1:3 or worse. Factor in caching, batching, and reserved throughput tiers. Red flag: any estimate that ignores output-heavy workloads 3. Context fidelity (lost-in-the-middle test) Bury a critical fact at position 40% of your max context. Ask the model to retrieve it. Most models degrade sharply for content that isn't at the start or end of a long context window. Target:>90% recall across all context positions 4. Hallucination rate on your domain Generic hallucination evals don't predict your failure mode. Build 50 domain-specific prompts where the correct answer is "I don't know." Count confident wrong answers. This number will surprise you. Target:<2% confident hallucinations on your eval set 5. Refusal rate on legitimate queries Over-refusal is a silent killer of user trust. Test edge-case but totally valid prompts in your domain — medical, legal, financial, security. High refusal rates on real use cases = high churn. Target:<3% false refusal on a representative query set 6. Tool use / function call reliability Ask the model to call a tool correctly across 100 prompts with varied phrasing. Check: correct tool selected, right arguments extracted, no hallucinated parameters. Parallel tool calls are a separate test. Target:>95% correct tool selection + arg extraction 7. Instruction-following consistency Give the model a system prompt with 5 constraints. Track how many it violates across 200 generations. Models that "mostly" follow instructions are unpredictable at scale — edge cases ship to prod. Target:<1% constraint violation rate 8. Output format stability If you're parsing structured output (JSON, XML, markdown tables), stress test it. Rephrasing the same prompt 50 ways and checking format compliance will reveal how brittle the model is without schema enforcement. Target:>98% valid structure without retries 9. Regression stability across model updates Ask your provider's update policy. Does the model change silently? Do you get versioned endpoints? A model that's great today and 10% worse next Tuesday because of a silent update is a production incident waiting to happen. Non-negotiable: pinned versioned endpoints in prod The trap most teams fall into: they evaluate on quality metrics only, then get surprised by cost overruns, latency spikes, or refusals in prod. Run this checklist before you commit. Change models after the fact and you're rewriting prompts, evals, and half your integration layer.

I went from 60 to 92% accuracy in an LLM-based app. At 60% accuracy, it felt like guessing. Now? 92%. This is what almost no one talks about. Investing in test infrastructure for LLMs pays off fast. In my most recent LLM project, I spent around 80% of our time doing something unexpected. It wasn’t fine-tuning models. It wasn’t experimenting with prompts. It was building the testing infrastructure. It involved: - designing a robust testing framework - automating test execution - populating it with real-world test cases from our customer It took me 4 weeks to buildthe test setup. 4 weeks where you have nothing to show. 4 weeks without any tangible results. But then we saw the real value. With the testing framework, we stopped working with a black-box.  - I immediately saw which scenarios performed poorly. - I could prioritize fixes based on actual failures. - I tested hundreds of prompt variants. All of this happening automatically and at scale. With testing infrastructure, we made the impactful shift. From manually testing prompts to massive prompt experiments. From relying on “vibe-driven” prompt engineering to measuring impact. From losing track of what actually improved the system to quick iterations. After hundreds of prompt iterations, just a tiny thing boosted the accuracy. We ended up adding 3 words to the initial system prompt. “Justify your answer” That alone improved our model’s performance from 60% to 92% accuracy. We discovered it because we tried hundreds of  things. We could try hundreds of things because testing was automated. Lessons learnt? 1// Don’t guess. Test.  - LLM behavior is unpredictable - Simple prompt changes can have huge impact But you won’t know unless you try many variants. 2// Automate early. Iterate Fast. - We spent 4 weeks building test infrastructure - Then just 2 days of human work to find the right solution Everything else (test running, metric tracking, prompt comparison) was automated. 3// Results Compound with Scale - Better test coverage leads to more reliable insights - More prompt variants results in higher chance of finding gold - Quicker feedback loops gives faster deliver and better product LM testing is not optional. It drives success by: - reducing guesswork - boosting performance - shortening development cycles - driving ROI LLMs don’t work until you test. And they won’t work well until you test at scale.

𝐓𝐡𝐞 𝐰𝐢𝐧𝐧𝐢𝐧𝐠 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐟𝐨𝐫 𝐞𝐚𝐫𝐥𝐲-𝐬𝐭𝐚𝐠𝐞 𝐀𝐈 𝐬𝐭𝐚𝐫𝐭𝐮𝐩𝐬 — 𝐏𝐚𝐫𝐭 𝟓/𝟓 𝐄𝐧𝐝-𝐭𝐨-𝐞𝐧𝐝 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐄𝐯𝐚𝐥𝐬. This is the final part of the series — and the most important. In Parts 1–4, the discussion was on design partners, engineers watching users, AI-generated code, and daily shipping. All of that gives you speed. This part is about making sure that speed does not destroy your product quality. 𝐓𝐡𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐰𝐢𝐭𝐡 𝐬𝐩𝐞𝐞𝐝. This is where most early-stage AI teams fall short. They can build fast. They can ship fast. But they break things as fast as they fix them. A prompt change that improves one use case quietly degrades three others. A model upgrade slowly degrades quality in ways nobody catches until a customer complains. With AI product outputs being probabilistic, "correct" is often a judgement call. And the same input can produce different outputs on different days if you change your prompts, your model version, or your retrieval pipeline. 𝐍𝐨𝐧-𝐝𝐞𝐭𝐞𝐫𝐦𝐢𝐧𝐢𝐬𝐭𝐢𝐜 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐚𝐠𝐞𝐧𝐭𝐢𝐜 𝐬𝐲𝐬𝐭𝐞𝐦. The winning teams build measurement and observability into every layer — dev, CI/CD, staging, and production. The eval framework is the measurement system. Before you even generate your first-line of code, you have to get the test and eval framework setup. It could be deterministic checks, deterministic UI automations, LLMs as judge for generated AI quality checks, and a way to score the results across agents and systems. 𝐓𝐡𝐞 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐫𝐚𝐭𝐜𝐡𝐞𝐭. The hardest thing I have seen across portfolio companies, is building an end-to-end system for a way to move code in an autonomous way from dev to stage to production, without worrying that things may have broken. The most important thing that compounds is bringing test cases from production to stage to development in reverse. The more robust your eval infrastructure, the faster you can move. 𝐓𝐡𝐞 𝐫𝐞𝐬𝐮𝐥𝐭 — 𝐚𝐥𝐥 𝐟𝐢𝐯𝐞 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫. When all five are in place — real design partners, engineers next to users, AI-generated code with senior control, daily shipping, and eval-instrumented testing — the product quality compounds at a rate that is almost impossible for competitors to match. You are building a machine that converts user insight into shipped product in days, with quality that improves with every iteration. 𝐈𝐌𝐏𝐎𝐑𝐓𝐀𝐍𝐓: If you are an early-stage AI founder and you do not have all five in place, please fix it now. Start with design partners — everything else flows from there. The product quality that wins markets is not built in a lab. It is built in the field, with real users, at speed, with a safety net that gets stronger every day. Love to hear your experience in parts or full, if you are practicing any of the above.

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.

After a good amount of time in software development and AppSec, it's been clear that you live and die by your test suite. Unit tests, integration tests, SAST, DAST....they all exist to catch the thing that bites you later in production So while integrating a LLM features into Clarus (my platform helping people break into cybersecurity), I began to wonder what the equivalent test harness is for catching drift in model responses? Enter Promptfoo. In this edition, I walk through how I'm using it to treat prompts like every other piece of production code (versioned, tested, and gated). A few things I cover: 👉 Why I point the harness at my live chat API, not a model in isolation. When a test fails, it failed against the thing users actually hit (retrieval, guardrails, API layer, all of it). 👉 The assertion philosophy: cheap deterministic checks first (regex, substring), expensive LLM-as-judge rubrics only where you must. 👉 Why an 80% failure rate on my first run wasn't a disaster. Errors and failures are completely different animals, and a green pipeline with failing assertions is fundamentally healthy. 👉 Sometimes a failure is a bad test, a real bug, or both like when a test that failed at 0.56 where the rubric scored a perfect 1.0. 👉 How the whole thing becomes a GitHub Actions merge gate, so a test suite stops being a suggestion and becomes an actual control. We're not inventing a new discipline for AI. We're applying the one we already have. The teams that ship LLM features safely will be the the ones who treated those prompts like production code.

Many developers treat testing as an afterthought when using AI coding agents. "Build this feature. Now write unit tests." The result? Green test suites that give a false sense of security. When you ask an agent to generate tests without constraints, it optimizes purely for green execution runs. And when you treat testing as a prompt engineering afterthought, you get afterthoughts for tests. Here is the shift that turns AI test suites into true architectural guardrails: Instead of asking for generic "tests," explicitly prompt your agent for 5 foundational test patterns: 1️⃣ Essential Tests (The 3 Basics) Don't let the agent stop at the happy path. Force it to write the 😊 Happy Path (ideal flow), the 😢 Sad Path (invalid formats and network timeouts), and extreme ⚡ Edge Cases (concurrency limits and stress parameters). 2️⃣ 🔗 Seam Tests If your agent builds Unit A and Unit B, the individual unit tests will pass. But the seam connecting them is where autonomous logic silently fractures. Prompt specifically for seam tests that verify the overlap. 3️⃣ 🧩 Conformance Tests Building modular plugin architecture? Force every module to honor the same behavioral contract so new additions don't deviate from the framework. The teams shipping reliable code with LLMs don't just prompt agents to write software faster. They prompt agents to verify software better. What's your go-to approach when generating tests?

Most teams chase the wrong trophy when designing evals. A spotless dashboard telling you every single test passed feels great, right until that first weird input drags your app off a cliff. Seasoned builders have learned the hard way: coverage numbers measure how many branches got exercised, not whether the tests actually challenge your system where it’s vulnerable. Here’s the thing: coverage tells you which lines ran, not whether your system can take a punch. Let’s break it down. 1. Quit Worshipping 100 % - Thesis: A perfect score masks shallow tests. - Green maps tempt us into “happy-path” assertions that miss logic bombs. - Coverage is a cosmetic metric; depth is the survival metric. - Klaviyo’s GenAI crew gets it, they track eval deltas, not line counts, on every pull request. 2. Curate Tests That Bite - Thesis: Evaluation-driven development celebrates red bars. - Build a brutal suite: messy inputs, adversarial prompts, ambiguous intent. - Run the gauntlet on every commit; gaps show up before users do. - Red means “found a blind spot.” That’s progress, not failure. 3. Lead With Edge Cases - Thesis: Corners, not corridors, break software. - Synthesize rare but plausible scenarios,multilingual tokens, tab-trick SQL, once-a-quarter glitches from your logs. - Automate adversaries: fuzzers and LLM-generated probes surface issues humans skip. - Keep a human eye on nuance; machines give speed, people give judgment. 4. Red Bars → Discussion → Guardrail - Thesis: Maturity is fixing what fails while the rest stays green. - Triage, patch, commit, watch that single red shard flip to green. - Each fix adds a new guardrail; the suite grows only with lessons learned. Core Principles: 1. Coverage ≠ depth. 2. Brutal evals over padded numbers. 3. Edge cases first, always. 4. Automate adversaries; review selectively. 5. Treat failures as free QA. Want to harden your Applied-AI stack? Steal this framework, drop it into your pipeline, and let the evals hunt the scary stuff, before your customers do.

Last week, a CTO of a biotech company asked me “How do you know if your agent is “good enough” to ship?” Great question. Here’s how I responded: CONTEXT They’re building an LLM-powered agent to assist with medical literature review by automating the initial scan of new publications, extracting key findings, and routing relevant insights to their R&D team. Knowing whether an agent is ready for production is one of the most common questions we get from product and compliance teams. The challenge is that traditional release criteria don’t apply cleanly to LLM agents. You’re not validating against a fixed spec. You’re validating a system that makes decisions, adapts to context, and occasionally fails in non-obvious ways. So what does “ready” look like? Here’s how we help teams make that call: 1) You’ve tested beyond the “happy path” Most agent testing stops at: did it complete the task? But in the real world, users are unpredictable. Inputs are messy. APIs break. Context changes. A production-ready agent needs to handle all of that. This means your evaluation suite needs to include: - Edge cases - Adversarial inputs - Tool/API failures - Context limit stress tests If you’ve only tested the scripted demo flow, you don’t yet know how the system will behave under pressure. 2) You’ve validated the failure modes with SMEs In regulated domains, how an agent fails matters more than how often it fails. If it hallucinates a treatment plan or makes up compliance language, that’s unacceptable—even if it only happens 2% of the time. Subject matter experts should review: - A sample of both correct and incorrect outputs - Failure cases across risk tiers - How well the system flags uncertainty or asks for help It’s not just about accuracy. It’s about defensibility and risk exposure. 3) QA isn’t a checklist, it’s a system Agents evolve. Prompts change. Models get upgraded. Your QA must be continuous. Before shipping, ask: - Do we have automated evaluations running in CI? - Can we detect regressions in reasoning, safety, or tool usage? - Is there a human-in-the-loop process for reviewing high-impact outputs? If something breaks post-launch, will you find out before your users or your regulators do? 4) You’ve documented how it works In regulated environments, readiness = governance + performance. Can you explain: - How the agent works (system design, model versions, tool integrations) - What you’ve tested and why - What safeguards exist (escalation, overrides, fallbacks) - How you’re monitoring and improving it in production Risk teams don’t just need metrics. They need confidence. TAKEAWAY In high-stakes domains, readiness isn’t a model score. It’s your ability to explain the agent’s behavior, trust how it fails, and continuously improve what it does next. You don’t need a perfect agent to ship. But you do need a reliable one. And a system around it that’s as mature as the tech inside it.

Here's the LLM evaluation stack I recommend to every team: Layer 1: Unit Tests (DeepEval) Stop treating AI as a mystery box. Integrate with Pytest to run assertions on every build. → Test individual components (retrievers, generators, tools) → Run in CI/CD to block regressions → Move from vibe-checking to deterministic engineering Layer 2: Metric Suite (50+ SOTA Metrics) Quantify performance with academic-grade metrics, not just "looks good" scores: → Hallucination: Is it making things up? → Faithfulness: Is it strictly grounded in your context? → Agentic Trajectory: Did it pick the right tool and use the correct arguments? → G-Eval: Define custom, subjective criteria in plain English. Layer 3: Synthetic Data Evolution Don't wait for user logs to find your bugs. → Generate thousands of "Golden" test cases from your docs in minutes → Automatically cover complex edge cases → Scale your testing without a single manual label Layer 4: Continuous Monitoring Evaluation doesn't stop at deployment. → Track performance drift in real-time → Get a "Rationale" (the why) for every production failure → A/B test prompt versions with statistical confidence DeepEval handles all 4 layers in one framework. One framework: ✓ 50+ research-backed metrics ✓ Pytest-native syntax ✓ Synthetic data generation ✓ Full Agent & RAG support This is how you ship AI with actual confidence. (100% Open-Source) GitHub Repo - https://lnkd.in/gQ3zCcZN Don't forget to ⭐️