RAG Pipeline Optimization
We diagnose where your RAG pipeline breaks and improve the path from chunking to retrieval to context assembly and answer grounding.
Your RAG pipeline retrieves wrong documents and your agents hallucinate
- Agents confidently cite information that is outdated, irrelevant, or simply not in your knowledge base
- Retrieval returns documents that are superficially similar but contextually wrong
- No way to measure retrieval quality until users flag the issue
- Context windows fill up with irrelevant chunks while the right information is excluded
- Knowledge base updates take too long to propagate, so agents serve stale information
How we solve this
We diagnose the failure point in your RAG pipeline and apply targeted fixes to chunking, embedding, retrieval, or context assembly.
RAG Diagnostic Report
End-to-end analysis of your pipeline: chunk quality, embedding performance, retrieval precision, and generation faithfulness
Semantic Chunking
Document-structure-aware chunking that preserves meaning and respects section boundaries
Embedding Optimization
Benchmarking and fine-tuning embeddings for your specific domain so retrieval quality improves where the current model is underperforming
Evaluation Framework
Automated pipeline testing retrieval quality, answer faithfulness, and answer relevance on every change
Incremental Indexing
Real-time knowledge base updates so your agents always serve current information
How it works
Pipeline Audit
We trace queries through your entire RAG stack and identify where quality degrades
Evaluation Setup
We build a labeled test set and automated eval pipeline to measure improvements objectively
Optimization Sprint
Targeted fixes to chunking, embeddings, retrieval, and context assembly - measured against baseline
Production Validation
Monitoring in production to confirm improvements hold under real traffic patterns
Frequently asked questions
What causes RAG hallucinations?
The most common causes: poor chunk quality (splitting documents at wrong boundaries), weak embeddings (that do not capture domain-specific semantics), stale index data (outdated information retrieved), and context assembly errors (right documents retrieved but assembled poorly). Our audit identifies exactly which of these affects your pipeline.
Do you support all vector databases?
Yes - Pinecone, Weaviate, Qdrant, Milvus, pgvector, ChromaDB, and custom solutions. The optimization techniques work regardless of your vector store choice.
How do you measure RAG quality?
We look at retrieval quality, answer grounding, and answer relevance with a mix of labeled evaluation sets and production trace review.
Can you help us build a RAG pipeline from scratch?
Absolutely. If you are starting fresh, we design and build the entire pipeline: document ingestion, chunking strategy, embedding selection, vector storage, retrieval logic, context assembly, and generation with citations. We also set up the evaluation framework from day one.
Ready to get started?
Book a consultation with a senior AI engineer. We will use the conversation to understand your requirements and suggest next steps.
Get a RAG Pipeline Assessment