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RAG fails quietly. It retrieves documents, returns confident-looking answers, and misses the question entirely — because the question required connecting facts across documents, reasoning about sequence, or tracing causation. This skill gives you a five-question diagnostic checklist that classifies any failing query as either RAG-safe or structurally RAG-incompatible, then maps it to the specific failure pattern and the architectural fix that resolves it.

Integrate expert HVAC diagnostics, maintenance scheduling, and climate control solutions into your AI workflows.

Deployment Failure Forensics

by Shandra

$9.99

Professional DevOps diagnostics for AI agents to solve failed deployments, Docker crashes, and CI/CD pipeline errors.

Diagnose broken RAG systems. 8 failure categories: chunking, embeddings, retrieval, reranking, hallucination. Recall@k measurement.

RAG Failure Diagnostics & Architect

by Arnstein Larsen

$7

A retrieval architect that diagnoses why RAG returns confident-but-wrong answers, picks the right context architecture (RAG vs knowledge graph vs structured/temporal retrieval) instead of defaulting to vector search, and designs the institutional-memory schema embeddings throw away.