Practical guides to LLM evaluation
No fluff. Learn how RAG evaluation works, how to build reliable judges, and how to wire evals into your pipeline.
How to Evaluate RAG Systems: The Complete Guide
The metrics that matter, how to build a golden dataset, and how to wire evaluation into your CI pipeline.
RAG Evaluation Metrics: Faithfulness, Relevance, Context Precision, and Recall
A practical guide to the RAG metrics that diagnose retrieval quality, generation quality, and hallucination risk.
LLM-as-a-Judge: A Practical Guide
The three modes, how to fight position and verbosity bias, choosing a judge model, and running it in production without cost blowout.
How to Build a Golden Dataset for LLM and RAG Evaluation
Examples, labels, metadata, versioning, and thresholds for turning ad hoc tests into repeatable evals.
How to Run LLM Evals in CI/CD
Block prompt, model, retrieval, and RAG code regressions with API-based evaluation gates.
Runtime LLM Evaluation: How to Score Production Outputs
How to evaluate chatbot replies, agent actions, and RAG answers after they are created without slowing users down.
LLM Evals for Lead Generation: Verify Messages Before They Ship
Check CRM support, compliance, and tone before generated outreach reaches prospects.
Self-Hosting eval.ninja: Complete Deployment Guide
Docker, Kubernetes, AWS ECS, Cloud Run, and serverless. Deployment examples with working config files.
Self-Hosted LLM Evaluation: When to Run Evals in Your Own Infrastructure
Privacy, BYOK, Docker deployment, cloud tradeoffs, and when self-hosting is worth it.
eval.ninja vs Promptfoo
Promptfoo is CLI-first with strong red teaming. eval.ninja is API-first with hosted scoring. Honest comparison of both.
eval.ninja vs DeepEval
DeepEval is Python-first and open source. eval.ninja is API-first with cloud and Docker self-hosting.
eval.ninja vs Evidently
Evidently is broad AI observability. eval.ninja is focused RAG and LLM evaluation through an API.