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 happens next
- 1. Context We review the situation and constraints.
- 2. Fit We recommend an appropriate next step.
- 3. Scope If relevant, we discuss scope.
Your RAG system retrieves the wrong evidence.
Internal knowledge bases, document Q&A systems, and AI support agents often use RAG. A system can still give wrong answers after the team tunes chunk size, changes overlap, or swaps the top-k parameter. Diagnosis needs to cover retrieval, ranking, context assembly, validation, and source quality, not chunk size alone.
Common symptoms: “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:
| Output | Decision It Supports |
|---|---|
| Root cause assessment | Which retrieval, ranking, context, or validation layer is causing the failure |
| Remediation table | Which fixes deserve attention first, with effort and expected quality impact |
| Fast fixes | Changes the team can test quickly without a full rebuild |
| Sprint-worthy items | Larger repairs that need implementation support, instrumentation, or evaluation design |
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 Readiness Review for broader system-level forensic review.
Engineering evidence
Codebase Analysis Agent: 30 Seconds to First Answer
ActiveWizards (Internal)
Language-aware chunking with Tree-sitter, FAISS vector retrieval, and LLM reasoning. 30 seconds from upload to first contextual answer on any codebase.
Competitor Intelligence Agent: Structured Research Workflow
ActiveWizards (Internal)
Multi-agent system for repeatable competitive analysis across pricing, features, and positioning with structured Pydantic-validated output.
Real-time anomaly detection processing 2.4M events/day with 70% fewer false positives
Mid-Market Healthcare Provider
How we built a real-time anomaly detection pipeline processing 2.4M events/day using Kafka, Isolation Forest, and foundation models. False positive rate reduced from 68% to under 20%.
Related articles
The RAG Audit Checklist: What to Evaluate Before Declaring a Retrieval Pipeline Production-Ready
A structured audit checklist for RAG pipelines covering ingestion quality, retrieval accuracy, generation faithfulness, observability, security, and per-query cost.
RAGRetrieval Quality Measurement: Metrics That Diagnose User-Facing RAG Failures
Which retrieval metrics diagnose different RAG failure modes, what each metric misses, and how to combine offline evaluation with production signals.
RAGThe RAG Failure Taxonomy: 12 Ways Production Retrieval Pipelines Break
A 12-part taxonomy for production RAG failures: retrieval, generation, ingestion, and operational failure modes that standard monitoring often misses.
Discuss your RAG Pipeline Audit path
Tell us about your system, the decision ahead, and the constraints. We will review the context and recommend the next step.
Direct contact with a principal engineer.