Eval Pipeline Architecture
Build an evaluation dataset from real use, pick the cheapest grader that works, measure the noise floor, and turn the suite into a release gate.
Offline evaluations assess LLM pipeline quality before deployment using curated datasets with known expected outputs. A golden dataset contains input + ideal output pairs representing critical cases. Automated metrics (exact match, ROUGE, semantic similarity, LLM-as-judge) score each sample. CI gates block deployment if eval scores drop. Offline evals catch regressions from prompt changes, model upgrades, or retrieval changes before they reach users.
The pipeline
Each stage in order — click any step to read what it does.
Interactive Pipeline Flow
Architecture diagrams
Offline eval pipeline with CI gate.
Key design decisions
The trade-offs worth knowing before you build this.
Any single metric can be gamed or may not reflect real quality. A well-curated golden dataset with diverse, representative examples is the foundation. Metrics are only as good as the dataset they run against.
Prompt changes often feel like improvements but subtly degrade specific cases. Without an eval gate, these regressions reach production silently. A CI eval gate catches them on every PR.
Low-scoring samples often cluster around specific input types. Are all chemistry questions failing? Are multi-hop reasoning questions wrong? These patterns reveal systematic issues to fix.
Continue reading
All AI Observability articles →LLM Evaluation Frameworks Compared
Promptfoo, DeepEval, RAGAS, LangSmith and Phoenix compared by what each is good at, with working examples and a way to choose.
Token Tracking and Cost Management
Monitor token usage and costs — per-request tracking, budgets, cost allocation across teams, and the optimization strategies that shrink the bill.
Phoenix: Open-Source LLM Observability
Arize's open-source, OTel-native LLM observability stack — traces, eval workflows, and prompt experiments, self-hosted with no data leaving your VPC.
Discussion
Sign in to share your feedback and join the discussion.