What are the Phases of a RAG (Retrieval-Augmented Generation) System?
While the inner workings of AI can seem like magic, a Retrieval-Augmented Generation (RAG) system actually follows a logical “store-then-retrieve” process. To understand how a self-hosted chatbot maintains accuracy, it helps to view it through two distinct phases: the Ingestion Phase and the Retrieval Phase.
Think of it like a library. The Ingestion Phase is the process of stocking the shelves and creating a card catalog, while the Retrieval Phase is when the librarian finds the right book to answer a specific question.
Phase 1: The Ingestion Phase (Preparation)
The Ingestion Phase is the background work that happens before a user ever types a question. During this phase, the system takes your raw data—documents, emails, or manuals—and prepares it for the AI to process later.
Key Steps:
- Data Collection: Gathering source files from local servers or cloud storage
- Chunking: Breaking large documents into smaller, manageable snippets to ensure the AI doesn’t need to read a 100-page manual to find one specific sentence
- Vectorization (Embedding): Converting text into vectors—numerical representations that capture the meaning of words
- Storage: Storing these vectors in a specialized vector database that enables conceptual searches rather than just keyword matching
Phase 2: The Retrieval Phase (Execution)
The Retrieval Phase is the live component of the system, triggered when a user submits a query. This phase finds relevant information and transforms it into a helpful answer.
Key Steps:
- Query Transformation: Converting the user’s question into a vector, similar to the document processing in the Ingestion Phase
- Similarity Search: Comparing the mathematical representation of the user’s question against stored documents to identify the most relevant snippets
- Contextual Generation: Providing these specific snippets to the Large Language Model (LLM) with instructions to answer based solely on the provided context
- Response Generation: Creating a natural-sounding answer based exclusively on your private data
Why This Distinction Matters
Separating these phases enables better performance optimization and troubleshooting:
- Ingestion Phase issues: If the chatbot can’t find information, the problem likely stems from poor data quality or ineffective chunking
- Retrieval Phase issues: If the chatbot finds information but explains it poorly, the issue may be with the prompt engineering or the LLM itself
Understanding this two-phase architecture helps identify and resolve problems more efficiently, ensuring your RAG system delivers accurate, relevant responses.