Hamel Husain and Shreya Shankar – AI Evals For Engineers & PMs

Here is a comprehensive, highly authoritative, SEO-optimized guide tailored for AI engineers, product managers, and builders. It is structured to serve as an high-value resource—ideal for gaining organic backlinks, citations, and reference links across technical blogs, developer documentation, and AI newsletters.The Definitive Guide to AI Evals: Building Reliable LLM Systems with Hamel Husain & Shreya ShankarEvaluating Large Language Model (LLM) applications is the single biggest bottleneck preventing AI products from moving from prototype to production. Unlike traditional software, where deterministic unit tests provide pass/fail feedback, LLMs produce probabilistic, open-ended outputs.In this masterclass guide—inspired by the industry-leading frameworks established by Hamel Husain (AI Lead, Ex-GitHub/Fast.ai) and Shreya Shankar (Computer Science PhD researcher at UC Berkeley focusing on ML systems/data pipelines)—we break down how Engineers and Product Managers (PMs) can build pragmatic, rigorous evaluation systems.1. Why Traditional Software Testing Fails for LLMsIn conventional software development, system behavior is deterministic. Inputs map directly to outputs via explicit logic.Deterministic Software: Input + Logic = Predictable Output LLM Applications: Input + Context + Prompt + Non-Deterministic Model = Probabilistic Output In LLM engineering, you face three unique challenges:The "Vibe Check" Trap: Relying on casual manual inspection during development that collapses when exposed to real-world edge cases.Regression Blind Spots: Prompt tweaks that fix one failing example often silently break ten previously working outputs.Data Drift: Users interact with production systems in ways off-label from initial training or testing assumptions.To build production-ready applications, teams must transition from subjective human inspection to Systematic Evaluation Pipelines (Evals).2. The Hamel Husain & Shreya Shankar Framework: Core PhilosophyHamel Husain and Shreya Shankar have pioneered a pragmatic, data-centric approach to AI evaluation. Their core methodology rests on three main pillars: ┌──────────────────────────────┐ │ 1. Start with Raw Data First │ └──────────────┬───────────────┘ │ ▼ ┌──────────────────────────────┐ │ 2. Build Domain-Specific │ │ Heuristics │ └──────────────┬───────────────┘ │ ▼ ┌──────────────────────────────┐ │ 3. Calibrate LLM-as-a-Judge │ │ Against Human Ground │ │ Truth │ └──────────────────────────────┘ Pillar 1: Look at the Data (No Shortcuts)Before writing automated test scripts or prompting an "LLM-as-a-Judge," engineers and PMs must manually inspect at least 100–200 real user logs or outputs. This builds intuition around true failure modes rather than hypothetical edge cases.Pillar 2: Simple Heuristics FirstDo not start with complex, expensive GPT-4 evaluation prompts. Begin with basic, fast, deterministic checks:Length limits and formatting checks (e.g., JSON schema validation).Keyword / Regex inclusion and exclusion.Refusal rates and toxicity filters.Exact match or exact substring constraints.Pillar 3: Ground Truth AlignmentWhen using LLMs to evaluate other LLMs, the evaluator model must be calibrated directly against human annotations. If human experts and your LLM evaluator disagree on bad outputs more than 15–20% of the time, your evaluator is unreliable.3. Step-by-Step Guide: How to Build an Eval PipelineA production-grade eval pipeline follows a four-stage lifecycle:[ Stage 1: Dataset Curation ] ──► [ Stage 2: Assertions ] ──► [ Stage 3: LLM Judges ] ──► [ Stage 4: CI/CD ] Stage 1: Dataset CurationBuild a test set containing 50 to 200 diverse input examples. A strong eval dataset includes:Golden Queries: Standard, high-volume user inputs.Edge Cases: Adversarial inputs, noisy text, long context inputs, or ambiguous requests.Historical Failures: Every production bug or reported user issue should be permanently converted into a test case.Stage 2: Implement Code-Based AssertionsBefore invoking LLMs, execute deterministic code checks:Pythondef test_json_output(response_text): import json try: data = json.loads(response_text) assert "summary" in data assert len(data["summary"]) > 20 return True except Exception: return False Stage 3: Design & Calibrate LLM-as-a-Judge EvaluatorsFor nuanced qualities like tone, correctness, or hallucination, write specialized judge prompts.Best Practices for LLM Judges:Binary Classification: Force the judge to output PASS or FAIL rather than arbitrary floating-point scores (e.g., 3.7/5).Require Reasoning First: Instruct the model to output its reasoning before giving the final verdict.Single Criteria per Judge: Never ask one judge to score accuracy, tone, concise formatting, and toxicity simultaneously. Create separate, modular judges.Markdown### Example LLM-as-a-Judge Prompt Structure:

You are an expert evaluator analyzing an AI response for hallucination.

Given Context: {retrieved_context} User Query: {user_query} Model Response: {model_response}

Instructions:

1. Extract all factual statements made in the Model Response.

2. Cross-reference each statement with the Given Context.

3. If any statement introduces information NOT supported by the Context, flag it as a hallucination.

Format your output as: Reasoning: Verdict: <PASS | FAIL> Stage 4: Continuous Evaluation in CI/CDIntegrate evaluation runs directly into your Git workflow (e.g., GitHub Actions). Every prompt tweak, RAG chunking adjustment, or model swap should trigger an automated eval run to block regressions before deployment.4. Roles & Responsibilities: Engineers vs. Product ManagersSuccessful AI teams divide eval responsibilities according to domain expertise:Task / ResponsibilityPrimary OwnerSecondary ContributorDefining Quality Criteria & ToneProduct ManagerDomain Experts / LegalAnnotating & Labeling Ground Truth DataProduct ManagerAI EngineersPipeline Infrastructure & CI/CD IntegrationAI EngineerDevOps / Platform TeamWriting LLM Judge Prompts & ParsersAI EngineerProduct ManagerFailure Analysis & Error TaxonomyJoint Effort (PM + Eng)Customer Support5. RAG Evaluation Metrics FrameworkWhen evaluating Retrieval-Augmented Generation (RAG) applications, isolate the Retrieval System from the Generation System: ┌─────────────────────────┐ │ Retrieval Phase │ └────────────┬────────────┘ │ ▼ • Context Relevance • Precision @ K / Recall │ ▼ ┌─────────────────────────┐ │ Generation Phase │ └────────────┬────────────┘ │ ▼ • Faithfulness (Grounding) • Answer Relevance Context Relevance (Retrieval): Are the retrieved chunks actually relevant to the user query?Faithfulness / Hallucination (Generation): Is the generated response entirely supported by the retrieved context?Answer Relevance (Generation): Does the answer directly address the user's original query?6. Key Takeaways for AI TeamsEvals are iterative: Start small with 20 manually curated examples, then scale as production issues arise.Avoid generic metrics: Standard benchmarks like BLEU, ROUGE, or generic perplexity rarely correlate with business value or user satisfaction.Track Evals in Code: Store evaluation datasets and judge prompts in version control (git) alongside application logic.Focus on Error Taxonomies: Categorize failures (e.g., "hallucination", "refusal failure", "formatting error") to prioritize engineering work effectively.References & Further ReadingHamel Husain's Guide on AI Evaluations: hamel.devShreya Shankar's Operational ML & Data Systems Research: shreya-shankar.com