Back to Module 1.11: LLM EvaluationComing Soon
AI Lesson & Submodule
Faithfulness and Hallucination Checks
Detect hallucinated statements in RAG outputs using source references.
Why This Matters
Preventing silent hallucinations is the number one requirement for enterprise RAG platforms.
What You Will Learn
- •Detect hallucinations
- •Calculate faithfulness metrics
- •Verify context grounding
Concepts Covered
Hallucination metricsFaithfulness scoring checksContext grounding validation
Mapped Foundation Project: LLM Evaluation Lab
Regression evaluation dashboard testing prompt templates against golden datasets, checking faithfulness and schema compliance.
Architecture Preview
CI/CD testing pipeline running prompt variants against tests, compiling scores, and flagging failures.
Prompt Template CommitGolden Dataset CollectionModel Completion Loop
Tech Stack Planned
TypeScriptVitestReact
GitHub: Coming SoonLive Demo: Coming Soon
Coming SoonTechnical Interview Value
- ?Detail how you calculate a faithfulness score by comparing model output claims against RAG context