How to Validate AI Model Outputs

Every product manager is racing to ship AI features. But here's what nobody talks about: most ship broken, get fixed quietly, or die slowly. The difference between shipping and shipping something that works? Evals. An eval = systematic way to measure if your AI output is actually good. If you want an AI feature that actually works for real users (not just in demos), evals are the most important thing you need to learn. These insight comes from Hamza Husein (ex-OpenAI, ex-Airbnb) and Shrea Shanker (ex-Atlassian, ex-GitHub), two of the sharpest minds in AI product management. Here’s a simple 5-step framework to get started 👇 1️/ Start with Error Analysis Generate 50 diverse outputs For each answer this: "Would I ship this? Yes or No?" For every "No," write why in 1-2 sentences Output: A list of 5 -10 recurring failure patterns. 2️/ Find Your Failure Modes Group similar errors together. Give each a clear name and note how often it appears. Example: Hallucination (12), Wrong Tone (18), Missing Context (8) Stop when you’ve reviewed around 20 more outputs without discovering any new failure types. Output: 3-5 named failure modes with counts 3/ Build Binary Rubrics Turn your top 3 failure modes into clear rubrics For each, define: → A pass/fail rule (no 1–5 ratings) → 3 examples of PASS → 3 examples of FAIL Example - Hallucination: PASS: Every fact is verifiable or clearly marked as inference. FAIL: Any unverifiable or made-up fact. Output: 3 rubrics with examples that define your quality bar. 4/ Test for Alignment Take 20 new outputs. You and a teammate score them independently using your pass/fail rules. Then calculate → (number of agreements) / 20. Target: 80 % + agreement. Below that? Your rubric is unclear. Refine the definitions or examples and test again. Output: Rubrics you can trust across the team. 5/ Diagnose & Fix with the Three Gulfs Now that you know your failure modes, it’s time to diagnose why they’re happening. There are only three reasons your AI feature isn’t working and each needs a completely different fix: Gulf #1 — Specification Problem → Fix with better prompting (days to fix) Gulf #2 — Knowledge Problem → Fix with RAG or retrieval (weeks to fix) Gulf #3 — Capability Problem → Fix with better models or fine-tuning (months to fix) Most teams reach for the wrong solution. In reality, 80% of problems are Gulf #1 (specification) but teams jump straight to Gulf #3 (fine-tuning) way too early. I’ll break down the complete Three Gulfs Framework with detailed examples and fixes in my upcomig posts. It’s dense enough to deserve its own deep dive. Liked this breakdown? Follow + Save for more no-fluff posts on how to build AI features that actually work.

Most AI apps don’t fail at “building.” They fail at “proving.” If your demo looks great but your outputs aren’t reliable, your app won’t stand out—especially in a challenge. Your AI can be brilliant… and still confidently wrong. That’s why evaluation is the missing layer between prototype and production. (The deck calls this out clearly: without evaluation you get unpredictable behavior + silent failures.) The “Report Card” that makes your AI app stand out When you ship (or submit) an AI app, test it like an exam—not like a vibe check. 1) Build your “exam” dataset Create 10–20 gold-standard examples (real questions + ideal answers). Include edge cases from real user behavior (confusing, incomplete, adversarial prompts). Generate variations to expand coverage. 2) Grade with a simple rubric Use a rubric like: Correctness (factually accurate?) Relevance (answers the question?) Hallucination (made-up content?) Contextual Relevancy (RAG) (did retrieval actually help?) Responsible AI (bias/toxicity?) 3) Combine machines + humans Automated checks = fast, repeatable, scalable Human review = gold standard for nuance Best principle: let people set the standard; let machines enforce it at scale. Why this matters for the Building AI Application Challenge In a room full of similar apps, evaluation is your differentiator: You don’t just claim “my bot is good” You show a report card, failure cases, improvements, and reliability metrics If you’re in the Building AI Application Challenge, don’t stop at “it works.” Add an Evaluation Report Card to your submission—this is how your app stands out to judges, recruiters, and real users.

I’m jealous of AI Because with a model you can measure confidence Imagine you could do that as a human? Measure how close or far off you are? here's how to measure for technical and non-technical teams For business teams: Run a ‘known answers’ test. Give the model questions or tasks where you already know the answer. Think of it like a QA test for logic. If it can't pass here, it's not ready to run wild in your stack. Ask for confidence directly. Prompt it: “How sure are you about that answer on a scale of 1-10?” Then: “Why might this be wrong?” You'll surface uncertainty the model won't reveal unless asked. Check consistency. Phrase the same request five different ways. Is it giving stable answers? If not, revisit the product strategy for the llm Force reasoning. Use prompts like “Show step-by-step how you got this result.” This lets you audit the logic, not just the output. Great for strategy, legal, and product decisions. For technical teams: Use the softmax output to get predicted probabilities. Example: Model says “fraud” with 92% probability. Use entropy to spot uncertainty. High entropy = low confidence. (Shannon entropy: −∑p log p) Language models Extract token-level log-likelihoods from the model if you have API or model access. These give you the probability of each word generated. Use sequence likelihood to rank alternate responses. Common in RAG and search-ranking setups. For uncertainty estimates, try: Monte Carlo Dropout: Run the same input multiple times with dropout on. Compare outputs. High variance = low confidence. Ensemble models: Aggregate predictions from several models to smooth confidence. Calibration testing: Use a reliability diagram to check if predicted probabilities match actual outcomes. Use Expected Calibration Error (ECE) as a metric. Good models should show that 80% confident = ~80% correct. How to improve confidence (and make it trustworthy) Label smoothing during training Prevents overconfident predictions and improves generalization. Temperature tuning (post-hoc) Adjusts the softmax sharpness to better align confidence and accuracy. Temperature < 1 → sharper, more confident Temperature > 1 → more cautious, less spiky predictions Fine-tuning on domain-specific data Shrinks uncertainty and reduces hedging in model output. Especially effective for LLMs that need to be assertive in narrow domains (legal, medicine, strategy). Use focal loss for noisy or imbalanced datasets. It down-weights easy examples and forces the model to pay attention to harder cases, which tightens confidence on the edge cases. Reinforcement learning from human feedback (RLHF) Aligns the model's reward with correct and confident reasoning. Bottom line: A confident model isn't just better - it's safer, cheaper, and easier to debug. If you’re building workflows or products that rely on AI, but you’re not measuring model confidence, you’re guessing. #AI #ML #LLM #MachineLearning #AIConfidence #RLHF #ModelCalibration

You've built your AI agent... but how do you know it's not failing silently in production? Building AI agents is only the beginning. If you’re thinking of shipping agents into production without a solid evaluation loop, you’re setting yourself up for silent failures, wasted compute, and eventully broken trust. Here’s how to make your AI agents production-ready with a clear, actionable evaluation framework: 𝟭. 𝗜𝗻𝘀𝘁𝗿𝘂𝗺𝗲𝗻𝘁 𝘁𝗵𝗲 𝗥𝗼𝘂𝘁𝗲𝗿 The router is your agent’s control center. Make sure you’re logging: - Function Selection: Which skill or tool did it choose? Was it the right one for the input? - Parameter Extraction: Did it extract the correct arguments? Were they formatted and passed correctly? ✅ Action: Add logs and traces to every routing decision. Measure correctness on real queries, not just happy paths. 𝟮. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝘁𝗵𝗲 𝗦𝗸𝗶𝗹𝗹𝘀 These are your execution blocks; API calls, RAG pipelines, code snippets, etc. You need to track: - Task Execution: Did the function run successfully? - Output Validity: Was the result accurate, complete, and usable? ✅ Action: Wrap skills with validation checks. Add fallback logic if a skill returns an invalid or incomplete response. 𝟯. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝘁𝗵𝗲 𝗣𝗮𝘁𝗵 This is where most agents break down in production: taking too many steps or producing inconsistent outcomes. Track: - Step Count: How many hops did it take to get to a result? - Behavior Consistency: Does the agent respond the same way to similar inputs? ✅ Action: Set thresholds for max steps per query. Create dashboards to visualize behavior drift over time. 𝟰. 𝗗𝗲𝗳𝗶𝗻𝗲 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗧𝗵𝗮𝘁 𝗠𝗮𝘁𝘁𝗲𝗿 Don’t just measure token count or latency. Tie success to outcomes. Examples: - Was the support ticket resolved? - Did the agent generate correct code? - Was the user satisfied? ✅ Action: Align evaluation metrics with real business KPIs. Share them with product and ops teams. Make it measurable. Make it observable. Make it reliable. That’s how enterprises scale AI agents. Easier said than done.

The AI workflow produced great results, yet people did not feel safe relying on the output. ⛔ That was the situation I encountered in a client workshop in Brussels last week, and it is far more common than most organisations like to admit. The team had invested time and effort into designing an AI-supported workflow. The use case was clear, the technical setup was sound, the data quality was acceptable, and the people involved had already received training on how to use AI. Despite all of this, the workflow was barely used in practice. People ran the AI step, reviewed the output, and then quietly redid the work themselves. During the workshop, we mapped the real workflow together, step by step, focusing not on how the process was documented but on how the work actually happened on a normal working day. At one point, a participant looked at the whiteboard and said: “I only trust the result after I have checked it myself anyway.” That sentence shifted the entire conversation. As we continued mapping the process, a pattern became visible: Everyone validated AI outputs differently.  Some checked everything, even low-risk drafts.  Others barely checked high-risk decisions. Accountability was assumed but never explicitly defined. Human validation was happening constantly, but it was invisible, inconsistent, and highly personal. We redesigned the workflow and introduced a simple checklist for built-in human validation. 💡 This checklist replaced individual safety habits with a shared, explicit process. ✅ Define the risk level of the output. Clarify whether the AI output is a draft, a recommendation, or a decision with external impact. ✅ Decide if validation is required. Make it explicit which outputs require human review and which can flow through without intervention. ✅ Specify the validation moment. Define when validation happens in the workflow and before which downstream step. ✅ Assign clear responsibility. Name the role that validates the output and the role that makes the final decision. ✅ Separate generation from judgment. Ensure the AI prepares content or options, while humans remain accountable for approval and outcomes. ✅ Remove unnecessary checks. Regularly review the workflow to eliminate validation steps that add friction without reducing risk. Once this checklist was applied, people felt much more confident about the AI output because they knew when human judgment was required. 👉 Is human validation in your AI workflows clearly designed, or is it still improvised? Let’s discuss.

User Feedback Loops: the missing piece in AI success? AI is only as good as the data it learns from -- but what happens after deployment? Many businesses focus on building AI products but miss a critical step: ensuring their outputs continue to improve with real-world use. Without a structured feedback loop, AI risks stagnating, delivering outdated insights, or losing relevance quickly. Instead of treating AI as a one-and-done solution, companies need workflows that continuously refine and adapt based on actual usage. That means capturing how users interact with AI outputs, where it succeeds, and where it fails. At Human Managed, we’ve embedded real-time feedback loops into our products, allowing customers to rate and review AI-generated intelligence. Users can flag insights as: 🔘Irrelevant 🔘Inaccurate 🔘Not Useful 🔘Others Every input is fed back into our system to fine-tune recommendations, improve accuracy, and enhance relevance over time. This is more than a quality check -- it’s a competitive advantage. - for CEOs & Product Leaders: AI-powered services that evolve with user behavior create stickier, high-retention experiences. - for Data Leaders: Dynamic feedback loops ensure AI systems stay aligned with shifting business realities. - for Cybersecurity & Compliance Teams: User validation enhances AI-driven threat detection, reducing false positives and improving response accuracy. An AI model that never learns from its users is already outdated. The best AI isn’t just trained -- it continuously evolves.

If I had to make LLM systems reliable in production, I wouldn’t start by adding more prompts. I’d focus on mastering these ideas: • Grounding outputs back to source data • Designing clear input and output contracts • Detecting when the model is uncertain • Validating structured outputs before use • Isolating failures so one bad call doesn’t break the system • Adding checkpoints instead of long fragile chains • Building retries with intent, not blind loops • Logging decisions, not just final answers • Evaluating behavior over time, not one-off responses None of this shows up in demos. All of it shows up in real systems. Most LLM failures aren’t “model issues”. They’re engineering discipline issues. If you care about deploying GenAI beyond notebooks, these are the skills that actually matter. #LLM #GenAI #AIEngineering #ProductionAI #SystemsDesign #Interviews #AI #Jobs Follow Sneha Vijaykumar for more... 😊

Ever wondered why some AI projects fail even with top engineers? It’s rarely about the code...... 𝗜𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝗮𝘀𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗳𝗶𝗿𝘀𝘁. Here’s what separates AI projects that deliver real value: 1️⃣ 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗙𝗿𝗮𝗺𝗶𝗻𝗴 𝗕𝗲𝗳𝗼𝗿𝗲 𝗠𝗼𝗱𝗲𝗹𝗹𝗶𝗻𝗴 Start with the business question: What decision will this AI support? Define success metrics upfront: false positive tolerance, revenue lift, conversion impact, regulatory compliance. Identify edge cases early: What happens when data is missing or input is anomalous? 2️⃣ 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 AI is only as good as the data it sees. Standardize transaction data, normalize categorical fields, enrich with external market signals. Ensure features align with regulatory constraints and risk policies. 3️⃣ 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗧𝗵𝗮𝘁 𝗠𝗮𝘁𝘁𝗲𝗿 Accuracy alone rarely matters in fintech. Focus on precision, recall, F1, and business impact metrics. Example: For fraud detection, high recall reduces missed fraud but increases operational cost. Balancing these trade-offs is product work, not just modeling. 4️⃣ 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗥𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 Model performance in development is rarely performance in production. Monitor drift, track input distribution, set automated alerts when metrics degrade. Establish human-in-the-loop checks for critical decisions. 5️⃣ 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗟𝗼𝗼𝗽𝘀 𝗮𝗻𝗱 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 Integrate AI outputs into workflows where users can validate or override results. Capture feedback in structured datasets for retraining. Track improvement over time, not just initial launch performance. 6️⃣ 𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘆 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 Document feature selection, model assumptions, and decision thresholds. Prepare audit logs and explainable AI outputs for regulators. Teams that treat AI as a product problem, not just a technical challenge, deliver faster, safer, and measurable results. Before investing in a new model, ask yourself: Are you solving the right problem, and do you know how success looks in the real world? ---------------------------- 🙋♂️ I help companies scale their product and engineering teams with experienced, hands-on engineers who start delivering immediately. Reach out if that’s what you need. 📥

Prompting isn’t the hard part anymore. Trusting the output is. You finally get a model to reason step-by-step… And then? You're staring at a polished paragraph, wondering:    > “Is this actually right?”    > “Could this go to leadership?”    > “Can I trust this across markets or functions?” It looks confident. It sounds strategic. But you know better than to mistake that for true intelligence. 𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗿𝗶𝘀𝗸: Most teams are experimenting with AI. But few are auditing it. They’re pushing outputs into decks, workflows, and decisions— With zero QA and no accountability layer 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗜 𝘁𝗲𝗹𝗹 𝗽𝗲𝗼𝗽𝗹𝗲: Don’t just validate the answers. Validate the reasoning. And that means building a lightweight, repeatable system that fits real-world workflows. 𝗨𝘀𝗲 𝘁𝗵𝗲 𝗥.𝗜.𝗩. 𝗟𝗼𝗼𝗽: 𝗥𝗲𝘃𝗶𝗲𝘄 – What’s missing, vague, or risky? 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 – Adjust one thing (tone, data, structure). 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 – Rerun and compare — does this version hit the mark? Run it 2–3 times. The best version usually shows up in round two or three, not round one.  𝗥𝘂𝗻 𝗮 60-𝗦𝗲𝗰𝗼𝗻𝗱 𝗢𝘂𝘁𝗽𝘂𝘁 𝗤𝗔 𝗕𝗲𝗳𝗼𝗿𝗲 𝗬𝗼𝘂 𝗛𝗶𝘁 𝗦𝗲𝗻𝗱: • Is the logic sound? • Are key facts verifiable? • Is the tone aligned with the audience and region? • Could this go public without risk? 𝗜𝗳 𝘆𝗼𝘂 𝗰𝗮𝗻’𝘁 𝘀𝗮𝘆 𝘆𝗲𝘀 𝘁𝗼 𝗮𝗹𝗹 𝗳𝗼𝘂𝗿, 𝗶𝘁’𝘀 𝗻𝗼𝘁 𝗿𝗲𝗮𝗱𝘆. 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗜𝗻𝘀𝗶𝗴𝗵𝘁: Prompts are just the beginning. But 𝗽𝗿𝗼𝗺𝗽𝘁 𝗮𝘂𝗱𝗶𝘁𝗶𝗻𝗴 is what separates smart teams from strategic ones. You don’t need AI that moves fast. You need AI that moves smart. 𝗛𝗼𝘄 𝗮𝗿𝗲 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗿𝘂𝘀𝘁 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗼𝘂𝘁𝗽𝘂𝘁𝘀? 𝗙𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 for weekly playbooks on leading AI-powered teams. 𝗦𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 to my newsletter for systems you can apply Monday morning, not someday.

If you're new to AI Engineering, you're likely: – forgetting to log or monitor system behavior – treating prompt engineering as an afterthought – ignoring API rate limits and blowing past quotas – trusting outputs without understanding model limitations – assuming models don’t need regular retraining or updates Let’s not have these mistakes hold you back. Follow this simple 45-rule checklist I’ve created to level up fast and avoid rookie mistakes. 1. Never deploy anything you haven’t personally tested. 2. Validate all AI responses for correctness and safety. 3. Always log inputs, outputs, and timestamps for traceability. 4. Keep your prompts and configurations under version control. 5. Track every API call, monitor quotas, usage, and latency. 6. Plan for outages, design fallback workflows for API failures. 7. Cache frequent queries, save money and reduce API calls. 8. Set clear timeout limits on external service requests. 9. Never assume the model “just works”, expect failure modes. 10. Review every line of code that interacts with the AI. 11. Sanitize all data before it hits your models. 12. Never save unverified model outputs to your database. 13. Monitor system health with real-time dashboards. 14. Keep secrets (API keys, tokens) away from your codebase. 15. Automate unit, integration, and regression tests for your stack. 16. Retest and redeploy models on a regular cadence. 17. Document every integration detail and model limitation. 18. Never ship features you can’t explain to your users. 19. Use JSON or structured data for model outputs, avoid raw text. 20. Benchmark latency and throughput under load. 21. Alert on anomalies, not just outright failures. 22. Test model outputs against adversarial, nonsensical, and edge-case inputs. 23. Track cost-per-query, and know where spikes come from. 24. Build feature flags to roll back risky changes instantly. 25. Maintain a “kill switch” to quickly disable AI features if needed. 26. Keep error logs detailed and human-readable. 27. Limit user exposure to raw or unmoderated model responses. 28. Rotate credentials and secrets on a fixed schedule. 29. Record and audit all changes in prompts, models, and data sources. 30. Schedule regular model evaluations for drift and performance drops. 31. Implement access controls for sensitive data and models. 32. Track and limit PII (personally identifiable information) everywhere. 33. Share postmortems and edge cases with your team, learn from mistakes. 34. Set budget alerts to catch runaway costs early. 35. Isolate test, staging, and production environments.