Diagnosing Rag Failure Modes

Problems It Solves

• Silent retrieval failure — RAG pipelines return plausible-sounding results on multi-hop and causal queries, making failures hard to detect. Teams iterate on embedding quality and chunking strategy for weeks before realizing the query type is the problem, not the implementation.

• Wrong fix applied — Most RAG debugging focuses on embedding models, chunk size, and reranking. These are the right levers for factual lookup failures. They do nothing for relational and temporal failures, where the architecture itself is mismatched to the query.

• Query type blindness — No standard vocabulary exists for distinguishing "what is X" from "how did X come to be" at the pipeline level. Without this distinction, every query gets routed to the same retrieval system regardless of structural fit.

• Scale degradation — RAG degrades on large corpora not because the embeddings get worse, but because the signal-to-noise ratio collapses. Teams add reranking layers and see marginal improvement, missing that tiered retrieval is the actual fix.

What You Get

• The two-class query taxonomy — A clear, actionable split between Class A (factual lookup, RAG-safe) and Class B (relational/temporal, RAG danger zone), with concrete examples of each so classification is fast and unambiguous.

• Five-question diagnostic checklist — Run any failing query through five yes/no checks (multi-document join required? order matters? causation chain? time span? why, not just what?) to score it as Class A, borderline, or Class B in under two minutes.

• Four named failure patterns — Multi-hop relational failure, temporal sequencing failure, organizational context failure, and scale failure — each with a symptom description, a worked example, and a specific architectural fix.

• Failure Classification Report template — A structured output artifact (query, class, failure patterns, root cause paragraph, recommended fix, references) that communicates a diagnosis clearly to engineers, architects, and non-technical stakeholders.

• Architectural fix references — Each failure pattern maps directly to a companion skill (designing-hybrid-context-layers, temporal-reasoning-sleuth, synthesizing-institutional-knowledge) so diagnosis connects immediately to remediation.

Who Should Use This

• Engineers and AI architects whose RAG pipeline is returning poor results and need to determine whether the problem is implementation quality (fixable with tuning) or architectural mismatch (requires a different retrieval approach).

• Teams building agents over organizational knowledge bases — ADRs, incident reports, policy documents, vendor contracts — where some queries will always be relational or temporal in nature.

• Technical leads evaluating whether to add a knowledge graph, timeline index, or hybrid retrieval layer and needing a principled basis for the recommendation rather than intuition.