HelekinRetrieval-Augmented Generation (RAG) Best Practices
AI Research Team
April 10, 2024
7 min read
The Problem with Raw LLMs
Raw LLMs are frozen in time and lack access to proprietary company data. RAG (Retrieval-Augmented Generation) solves this by dynamically injecting relevant context into the LLM's prompt at runtime.#
The RAG Pipeline
1. **Ingestion & Chunking**: Document parsing must be intelligent. Do not blindly chunk by character count; chunk by semantic meaning (paragraphs, sections).2. **Embeddings & Vector Search**: We utilize OpenAI's `text-embedding-3-large` model to convert text into high-dimensional vectors, stored in Pinecone or pgvector.
3. **Retrieval**: Implement hybrid search (Dense Vector + Sparse Keyword search) to maximize recall accuracy.
4. **Generation**: Instruct the LLM strictly: *'Answer the question based ONLY on the provided context.'*
*Enterprise RAG requires rigorous evaluation pipelines to measure relevance and factual accuracy over time.*