What does this article cover?

A practical method to debug RAG failures by separating retrieval problems from grounding and generation problems.

Engineering and product teams operating RAG features who need a repeatable triage and fix process.

RAG Root Cause Analysis: Debugging Wrong Answers Systematically

When a RAG system gives a wrong answer, the fastest path to a fix is to avoid arguing about the model. Instead, treat the answer as a symptom and isolate which layer failed.

Step 1: Check whether the system had evidence

If the answer has no citations or sources, the system may have skipped retrieval, retrieved nothing, or been prompted to answer anyway. Start by verifying retrieval was attempted (see citations and grounding).

Step 2: Inspect retrieval results before generation

Look at the top retrieved chunks. Ask two questions:

Recall. Did the correct source appear anywhere in the candidates?
Relevance. Were the top results actually relevant to the intent?

If recall is low, treat it as a retrieval problem (see retrieval quality and common RAG failures).

Step 3: Validate chunking and metadata

Many failures come from ingestion and metadata:

Chunks are too large or too small.
Key context is split across chunks without headers.
Missing ownership, taxonomy or effective dates (see metadata strategy).

Use ingestion-time controls like deduplication and stable identifiers to reduce noise (see ingestion pipelines and deduplication).

Step 4: Check permissions and filters

If the correct source exists but never appears, it may be filtered out by permissions or tenancy boundaries. Verify ACL metadata and query-time filters (see RAG permissions).

Step 5: Review ranking and query rewriting

If relevant content is present but buried, focus on ranking: hybrid search, reranking, and query rewriting strategies (see ranking and relevance).

Step 6: Evaluate grounding and generation behaviour

If evidence is good but the answer is still wrong, the model may be misreading sources or blending conflicts. Improve grounding prompts, require citations for key claims, and use structured outputs where appropriate (see structured outputs).

Make fixes durable

Once fixed, convert the incident into a test:

Add a golden query that must retrieve the correct source (see synthetic monitoring).
Add a regression case so prompt changes do not reintroduce the issue (see regression testing).

RAG improves fastest when debugging is systematic and evidence-driven.