RAG Implementer

Build production-ready retrieval-augmented generation systems. RAG = Retrieval + Context Assembly + Generation. Use RAG when LLMs need access to fresh, domain-specific, or proprietary knowledge not in their training data. Do not use RAG when simpler alternatives (FAQ pages, keyword search, semantic search) suffice. For KB architecture selection and governance, use the knowledge-base-manager skill. For knowledge graph implementation, use the knowledge-graph-builder skill.

Overview

Before building RAG, validate the need: try FAQ pages, keyword search, concierge MVP, or simple semantic search first. Only proceed with RAG for 50k+ documents with validated user demand and $200-500/month budget. RAG systems range from Naive (prototype) through Advanced (production) to Modular (enterprise), each tier adding complexity and cost.

The RAG pipeline has three core stages. First, retrieval finds relevant documents using hybrid search (semantic + keyword). Second, context assembly ranks, deduplicates, and compresses retrieved chunks into an optimal prompt. Third, generation produces a grounded response with source attribution. Each stage has distinct failure modes: retrieval can miss relevant documents (low recall), context assembly can overwhelm the model (lost in the middle), and generation can hallucinate despite good context (low faithfulness).

Modern RAG extends beyond basic vector similarity. Hybrid search combining dense embeddings with sparse BM25 is now the baseline. Re-ranking with cross-encoders improves precision after initial retrieval. Contextual chunking and late chunking preserve document-level semantics that fixed-size chunking loses. GraphRAG enables multi-hop reasoning over entity relationships by building knowledge graphs from documents. Proposition chunking breaks documents into atomic facts for precise retrieval of individual claims.

Choose techniques based on your query complexity and document structure. Start with hybrid search and re-ranking as the foundation, then layer contextual chunking, GraphRAG, or query expansion as needed. Measure everything: Precision@K, Recall@K, faithfulness, and end-to-end latency. The difference between a good and bad chunking strategy alone can create a 9% gap in recall performance.

Quick Reference

Common Mistakes

Embedding Model Notes

text-embedding-3-large (3072 dimensions) remains OpenAI's most capable embedding model. It supports Matryoshka dimensionality reduction via the dimensions API parameter -- 1024 dimensions often delivers near-full accuracy at one-third storage cost. text-embedding-3-small (1536 dimensions) is a cost-effective alternative at $0.02 per million tokens. For code search, Voyage Code 3 outperforms general-purpose models. For multilingual workloads, consider multilingual-e5-large or Cohere embed-v4. Always benchmark on your domain data; general benchmarks do not predict domain-specific performance.

Vector Store Notes

Pinecone for managed simplicity, Weaviate or Qdrant for self-hosted with hybrid search, Chroma for prototyping, pgvector for teams already on PostgreSQL (practical limit around 10-100M vectors), and Milvus/Zilliz for billion-scale deployments. Choose index type based on tradeoffs: HNSW for speed (higher memory), IVF for scale (requires training), flat for exact search on small datasets only.

Most vector databases now achieve 10-100ms query latency on 1-10M vector datasets. Start with the simplest option that fits your scale requirements and migrate only when you hit concrete performance limits.

Delegation

• Discover data sources and assess knowledge base quality: Use Explore agent to catalog documents, evaluate data freshness, and identify authoritative content
• Implement retrieval pipeline with hybrid search and re-ranking: Use Task agent to build embedding, indexing, retrieval, and evaluation components
• Design RAG architecture and vector store topology: Use Plan agent to select embedding models, vector databases, chunking strategies, and deployment architecture

For KB architecture selection, curation workflows, and governance, use the knowledge-base-manager skill. For knowledge graph implementation (ontology, entity extraction, graph databases), use the knowledge-graph-builder skill.

References

• Architecture patterns and prerequisites
• Chunking strategies and knowledge base design
• Retrieval methods and pipeline design
• Evaluation metrics and quality gates
• Production deployment and continuous improvement