The original title is "How to Build an LLM Eval Pipeline for Your AI App in 2026"

Short summary

LLM apps fail silently at the semantic level where unit tests cannot catch factual errors, tone issues, or quality drift. A practical eval pipeline combines heuristic checks on every PR, LLM-as-judge scoring nightly, and human evals for golden dataset curation. Tools like PromptFoo, Braintrust, and Inspect help automate the harness, with a recommended 90% pass-rate threshold to block deployments.

• •Three eval layers: heuristic checks (fast, objective), LLM-as-judge (subjective rubric scoring), and human evals (ground truth calibration)
• •Start with 100 real production requests, manually review 50 for ground-truth baseline, then script a basic test runner with one heuristic check
• •Run heuristic tests on every PR and full LLM-judge evals nightly in GitHub Actions; block deployments below 90% pass rate

Generated with AI, which can make mistakes.