How to Build an LLM Eval Pipeline for Your AI App in 2026

A developer outlines how to build an LLM evaluation pipeline for AI applications, covering heuristic evals, LLM-as-judge, and human evals. The approach starts with a minimal Python script and a golden dataset of 500-1000 test cases, automated via GitHub Actions to block deployments when pass rates drop below 90%.

LLM applications fail silently at the semantic level. Standard unit tests verify that functions return values, but they cannot detect if the output is factually wrong, off-tone, or missing steps. Evals fix this by running a prompt, inspecting the output, and programmatically or judgmentally deciding its quality. Unit tests break on non-determinism. Temperature-driven randomness means identical inputs produce varying valid outputs, which defeats static equality checks. Semantic equivalence "Paris is the capital" vs. "Capital is Paris" makes string matching useless. Small model or prompt updates cause gradual quality drift that only surfaces at scale. Tone and accuracy require judgment that basic assertions cannot encode. 1. Heuristic Evals check measurable properties: JSON validity, word counts, PII presence. They are fast and objective — ideal for regression gates on every pull request. 2. LLM-as-Judge uses a second LLM to grade output against a rubric. Best for subjective content too complex for code but too vast for manual review. 3. Human Evals are the ground truth. Use them to build your golden dataset, calibrate the LLM judge, and finalize decisions before major releases. Start minimal: a Python script, a JSON file of test cases, and a loop comparing LLM output against assertions or a rubric. Curate a golden dataset of 500–1000 production-representative requests including edge cases and adversarial prompts. Refresh it quarterly to match user behavior drift. Automate the harness in GitHub Actions. Block deployments when pass rates drop below your threshold 90% is a common starting point . Run fast heuristic tests on every PR and full LLM-judge evals nightly. Pull 100 real requests from production. Manually review 50 to build a ground-truth baseline. Script a basic test runner. Implement one heuristic check. This gives you more safety than zero automated testing and a foundation to build on.