RAG Knowledge Base Auditor

The problem

RAG systems often fail due to poor document preparation, mechanical chunking that breaks context, and stale source material. Teams struggle to diagnose why chatbots produce ungrounded, uncited, or outdated answers despite having a large knowledge base.

What it does

• Identifies source gaps and document quality issues that lead to retrieval failures.
• Evaluates chunking logic and metadata strategies to improve context preservation.
• Audits citation accuracy and answer grounding to ensure traceability to source material.
• Generates failure mode analyses and remediation plans for incomplete or conflicting sources.
• Produces structured audit reports with test queries and deployment readiness checklists.

Frameworks & tools

Compatible with any RAG architecture including vector databases (Pinecone, Weaviate, Milvus), orchestration layers (LangChain, LlamaIndex), and embedding models.

Why this beats prompting it yourself

Generic prompts often miss the technical nuances of how data parsing impacts retrieval. This skill enforces a rigorous, multi-step diagnostic framework that specifically targets common RAG failure points like metadata filtering and semantic coherence.

Use cases

• Audit a support bot knowledge base before production deployment.
• Diagnose why a policy chatbot is retrieving outdated document versions.
• Review document chunking strategies to better handle tables and exceptions.
• Create a regression test suite for a RAG system after updating the source set.

Known limitations

Cannot access live vector databases directly, reports are based on user-provided system details and logs. Does not perform OCR on images or complex PDF scans.