--- title: AI Evaluation Engineering: Build a Production-Grade LLM... description: A comprehensive handbook for building a production-grade LLM evaluation platform from scratch. Covers eval-driven development methodology, a three-tier... canonical: https://daily.dev/posts/ai-evaluation-engineering-build-a-production-grade-llm-evaluation-platform-from-scratch-full-handb-ju9v0rogq twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: AI Evaluation Engineering: Build a Production-Grade LLM Evaluation Platform from Scratch [Full Handbook] | daily.dev og:description: A comprehensive handbook for building a production-grade LLM evaluation platform from scratch. Covers eval-driven development methodology, a three-tier... og:url: https://daily.dev/posts/ai-evaluation-engineering-build-a-production-grade-llm-evaluation-platform-from-scratch-full-handb-ju9v0rogq og:image: https://api.daily.dev/og/posts/ju9V0RoGq.png og:image:alt: AI Evaluation Engineering: Build a Production-Grade LLM Evaluation Platform from Scratch [Full Handbook] og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # AI Evaluation Engineering: Build a Production-Grade LLM Evaluation Platform from Scratch [Full Handbook] **[freeCodeCamp](https://daily.dev/sources/freecodecamp)** · 67 min read · 6 upvotes · 0 comments ## Summary A comprehensive handbook for building a production-grade LLM evaluation platform from scratch. Covers eval-driven development methodology, a three-tier evaluation architecture (offline dataset evaluation, CI/CD regression gates, and online production monitoring), golden dataset curation from production failures, six core RAG metrics (faithfulness, context recall, context precision, answer relevancy, hallucination, groundedness), LLM-as-judge calibration, agentic system evaluation, and automated CI/CD eval gates. Includes full working Python code and a companion GitHub repository. Uses a legal RAG application as a running example to illustrate how missing context recall metrics can cause silent production failures even when other metrics appear healthy. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is context recall in RAG evaluation and why is it different from faithfulness? Context recall measures whether a retriever returned all the information needed to answer a question, while faithfulness measures whether the generated answer is grounded in whatever context was retrieved. A system can score 0.95 on faithfulness while context recall sits at 0.52, meaning the answer is perfectly grounded but built from incomplete retrieved information, producing confidently wrong outputs. _Teams debugging silent RAG failures compare metric tradeoffs and platform writeups on daily.dev._ ### How do you calibrate an LLM-as-judge evaluator before trusting it in a CI pipeline? Collect 50 to 100 human-labelled examples spanning the quality spectrum (roughly 10 excellent, 10 poor, 30 ambiguous), run the judge on all of them, and compute Spearman's rank correlation against human scores. A correlation above 0.80 with a p-value below 0.05 is considered production-ready; also check mean absolute error and systematic bias before letting the judge block merges. _Developers building trustworthy AI evaluation pipelines track calibration techniques on daily.dev._ ### What is the recommended sample rate and model choice for cost-effective production LLM monitoring? Sample around 5 to 10 percent of production traces rather than evaluating every one, use gpt-4o-mini for most metrics, and reserve a stronger model like gpt-4o specifically for hallucination detection since it requires deeper factual reasoning. This combination keeps LLM-judged evaluation costs manageable, roughly $0.001 to $0.003 per test case, while preserving diagnostic power. _Engineers balancing AI evaluation costs against coverage follow practical cost strategies on daily.dev._ ## Similar posts on daily.dev - [Production-Ready LLM Agents: A Comprehensive Framework for Offline Evaluation](https://daily.dev/posts/production-ready-llm-agents-a-comprehensive-framework-for-offline-evaluation-5dvqdy96n) · Towards Data Science · 1 upvotes · 0 comments - [Choosing Effective LLM Evaluation Strategies for AI Teams](https://daily.dev/posts/choosing-effective-llm-evaluation-strategies-for-ai-teams-zboitrqp2) · PromptLayer Blog · 0 upvotes · 0 comments - [How to Build a Self-Evaluating AI System: Automated Testing and Evaluation Pipelines for LLM Applications](https://daily.dev/posts/how-to-build-a-self-evaluating-ai-system-automated-testing-and-evaluation-pipelines-for-llm-applica-yppnqlvra) · freeCodeCamp · 1 upvotes · 0 comments - [Beyond LLM-as-a-judge: Establishing LLM evaluations as a foundation for trustworthy agentic AI systems](https://daily.dev/posts/beyond-llm-as-a-judge-establishing-llm-evaluations-as-a-foundation-for-trustworthy-agentic-ai-syste-m8kymtr9x) · Dynatrace · 1 upvotes · 0 comments - [How to evaluate LLMs before production](https://daily.dev/posts/how-to-evaluate-llms-before-production-ps4g4zhbm) · GitHub Blog · 2 upvotes · 0 comments --- Tags: [#python](https://daily.dev/tags/python), [#llm](https://daily.dev/tags/llm), [#cicd](https://daily.dev/tags/cicd), [#rag](https://daily.dev/tags/rag) [View this post on daily.dev](https://daily.dev/posts/ai-evaluation-engineering-build-a-production-grade-llm-evaluation-platform-from-scratch-full-handb-ju9v0rogq) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"AI Evaluation Engineering: Build a Production-Grade LLM Evaluation Platform from Scratch [Full Handbook]","url":"https://daily.dev/posts/ai-evaluation-engineering-build-a-production-grade-llm-evaluation-platform-from-scratch-full-handb-ju9v0rogq","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/ai-evaluation-engineering-build-a-production-grade-llm-evaluation-platform-from-scratch-full-handb-ju9v0rogq"},"datePublished":"2026-08-10T23:28:38.908Z","dateModified":"2026-09-14T08:05:48.230Z","description":"A comprehensive handbook for building a production-grade LLM evaluation platform from scratch. 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