Retrieval-Augmented Generation (RAG) grounds Large Language Models in proprietary corporate knowledge, eliminating hallucinations while avoiding the immense cost of fine-tuning foundational models.
1. Document Ingestion & Recursive Chunking
Raw text documents must be parsed and partitioned into manageable semantic chunks. Using recursive character splitting with small overlap (e.g. 500-token chunks with 50-token overlap) guarantees that sentences spanning boundary lines retain coherent meaning.
2. Vector Indexing & Hybrid Search
Generating high-dimensional embeddings (via OpenAI text-embedding-3 or HuggingFace models) and storing them in vector stores like ChromaDB, Qdrant, or PGVector allows nearest-neighbor cosine similarity queries.
3. Context Injection & Re-Ranking
Applying a cross-encoder model to re-score candidate chunks before feeding them into the prompt context window drastically reduces irrelevant citations and improves answer fidelity.