Pre-trained language models do not know about your internal client documents, custom software codebases, or NDIS service agreements. Retrieval-Augmented Generation (RAG) bridges this gap by injecting relevant company context directly into model prompts before executing generation queries.
1. Vector Search Embeddings
Convert your unstructured documents (PDFs, text files, logs) into numerical vectors using embedding models. When a user asks a question, perform a vector lookup in databases like Pinecone or pgvector to extract the most relevant paragraphs.
Vector databases index semantic relationships rather than exact keywords, allowing the retrieval engine to pull relevant sections even if the user phrased the query using synonyms.
2. Chunking Strategies
Never dump entire files into vector databases in one go. Break files down into semantic chunks (e.g., 500 characters with 10% overlap) to preserve local context and ensure retrieval accuracy.
3. Context Filtering & Prompt Engineering
Inject retrieved chunks into the prompt window alongside strict boundaries: Answer the question ONLY based on the provided text. If you don't know, say 'Not found'. This prevents model hallucinations and secures system accuracy.