RAG Pipeline: A Production Guide
RAG Pipeline: A Production Guide
Retrieval-Augmented Generation (RAG) is one of the most practical AI patterns today. Instead of relying solely on an LLM's training data, you augment it with your own documents at query time. The result: accurate, grounded, up-to-date responses without fine-tuning.
At KYFEX, we've built RAG pipelines for multiple clients across healthcare, government, and SaaS. Here's what we've learned about taking RAG from prototype to production.
The Architecture
Our production RAG stack:
- LLM: Managed AI model — strong reasoning, large context window, native cloud integration
- Vector Store: Managed OpenSearch Serverless or pgvector on Aurora Serverless
- Ingestion: Lambda functions processing documents → chunking → embedding → vector store
- Retrieval: Semantic search with metadata filtering, re-ranking for relevance
- Serving: API Gateway + Lambda for low-latency inference
Key Lessons
1. Chunking Strategy Matters More Than Model Choice
We tested chunk sizes from 256 to 2048 tokens. The sweet spot for most document types was 512-768 tokens with 100-token overlap. But the real insight: different document types need different chunking strategies.
2. Hybrid Search Beats Pure Vector Search
Combining vector similarity search with keyword matching (BM25) consistently improved retrieval accuracy by 15-20% in our benchmarks. Most RAG tutorials skip this.
3. Context Window Management
A large context window is a blessing, but stuffing it with retrieved chunks is not the answer. We cap at 5-8 relevant chunks and use a re-ranker to ensure quality over quantity.
Results
For a recent client deployment:
- 95%+ answer accuracy on domain-specific questions
- Sub-2-second response time including retrieval and generation
- Zero hallucinations on factual queries within the knowledge base
What's Next
We're exploring agentic RAG patterns where the LLM decides when and what to retrieve, rather than always retrieving. Early results are promising.
Building a RAG pipeline? Book a technical deep dive and we'll review your architecture.