RAG Pipeline Audit
We audit the core layers of your RAG pipeline, rank what is causing the quality failure, and turn failing queries into a concrete remediation path.
What you get back
- 1. Diagnosis What works, what is blocked, and why.
- 2. Recommendation Audit, advisory, sprint, or pause.
- 3. Scope Next action, boundaries, and timing.
Your RAG system retrieves the wrong evidence.
Every company that built an internal knowledge base, document Q&A system, or AI support agent used RAG. Many of them underperform once real users ask messy questions. The team tuned chunk size, changed overlap, swapped the top-k parameter. The system still gives wrong answers. The root cause usually sits across retrieval, ranking, context assembly, validation, or source quality, and chunk size is rarely the whole answer.
Common complaints that signal this buyer: “our retrieval isn’t finding the right chunks,” “it’s hallucinating even when the answer is in the docs,” “we changed the chunk size and it got worse,” “re-ranking didn’t help.”
What We Audit
| Layer | What We Assess |
|---|---|
| Chunking strategy | Chunk size, overlap, splitting method (fixed, semantic, structural). Are chunks preserving meaning or splitting across logical units? |
| Embedding model | Is the embedding model appropriate for the domain and query type? Retrieval accuracy test vs. alternatives. |
| Retrieval pipeline | Vector search configuration, similarity metric, top-k tuning, hybrid search (vector + keyword). Are the right chunks being retrieved? |
| Re-ranking | Is a re-ranker in place? Is it calibrated to the domain? Does it improve or degrade precision? |
| Context assembly | How are retrieved chunks assembled into the prompt? Is there deduplication? Is the context window being used efficiently? |
| Generation and validation | Is the final answer validated against retrieved context? Is there a hallucination detection step? |
We construct a golden dataset from your own failing queries and test retrieval precision at each layer. Every finding is quantified as evidence.
Common Failure Patterns
| Pattern | Symptom | Root Cause | Fix |
|---|---|---|---|
| Semantic split | Splits a sentence across chunks | Fixed-size chunking ignores structure | Semantic chunking |
| Wrong embedding model | Generic queries retrieve better than domain queries | Model not trained on domain vocabulary | Domain-specific or fine-tuned model |
| Top-k too low | Correct answer in corpus but not retrieved | k=3 misses relevant chunk at position 4 | Increase k, add re-ranking |
| Re-ranker miscalibrated | Re-ranker moves correct chunk lower | Cross-encoder not fine-tuned for domain | Fine-tune or swap re-ranker |
| Context window stuffed | LLM sees too much context, loses the answer | No deduplication or relevance threshold | Context window optimization, dedup |
| No output validation | LLM hallucinates despite correct retrieval | No grounding check on final output | Hallucination detection gate |
What you leave with
Written audit report:
- Root cause assessment: which layer is causing the failure
- Ranked remediation table: fix, projected quality improvement, effort
- Quick wins implementable in <1 week
- Sprint-worthy items requiring AW implementation
Best Fit
- Production RAG system is missing quality expectations
- Users complain the system gives wrong answers
- Engineering team tuned chunk size, overlap, and top-k, then ran out of ideas
- Leadership is asking why the system underperforms the demo
For teams looking for a RAG pipeline audit, the work centers on concrete RAG quality problems and retrieval accuracy improvement.
Better Routed Elsewhere
- There is no failing query sample to test
- The system is still a concept rather than a working RAG pipeline
- The only ask is vector database selection before the team has mapped retrieval, re-ranking, context assembly, and validation
How We Engage
| Engagement | What You Get |
|---|---|
| RAG Pipeline Audit | Scoped assessment. Written report and findings call covering failing queries, root causes, and remediation order. |
| RAG Fix Sprint | Requires audit first. Implements top-ranked items and installs an evaluation harness with a golden dataset for ongoing quality measurement. |
| RAG Quality Retainer | Ongoing quality assessment for evolving corpora, drift detection, and recurring review. |
Related
Also see: Production AI Audit — for broader system-level forensic review.
Deployments in this area
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Discuss your RAG Pipeline Audit path
Send the system context, constraints, and pressure. A Principal Engineer reviews it and recommends the next step.
No SDRs. A Principal Engineer reviews every submission.