How Does the ‘Retrieval Phase’ Work in a RAG System?
The Retrieval Phase is the “active” side of a RAG system. While the Ingestion Phase prepares and stores data, the Retrieval Phase activates the moment a user types a question into the chat box.
This phase bridges the gap between a user’s natural language query and the vast amount of technical data stored in your vector database. It ensures that the AI doesn’t simply guess an answer but bases its response on the most relevant available facts.
How Retrieval Works: Step-by-Step
The transition from question to answer happens in a fraction of a second through this specific sequence:
1. Query Vectorization
When you ask a question like “What is our company’s holiday policy?”, the system doesn’t search for those exact words. Instead, it sends your question through the same embedding model used during the Ingestion Phase, transforming your question into a mathematical query vector.
2. The Similarity Search
With your question now represented as a vector (a string of numbers), the system compares it against all document chunks stored in your vector database.
The system performs a mathematical comparison—often called a “cosine similarity” search—to find which stored vectors are closest in meaning to your query vector. Even if your document says “vacation time” and you asked about “holiday policy,” the system will identify them as a match because their mathematical vectors are similar.
3. Top-K Retrieval
Rather than retrieving just one piece of information, the system typically pulls the “Top-K” most relevant results (for example, the top 3 or 5 most similar text chunks). These text snippets serve as the raw evidence the AI will use to formulate its response.
4. Augmenting the Prompt
This step puts the “Augmented” in Retrieval-Augmented Generation. The system takes the retrieved text chunks and incorporates them into a prompt along with the original user question.
A simplified version of this prompt looks like this:
Using only the following pieces of context, answer the user’s question.
If the answer isn’t in the context, say you don’t know.Context: [Retrieved Chunk 1], [Retrieved Chunk 2]
Question: [User’s original question]
5. Generation
Finally, this enriched prompt goes to the Large Language Model (LLM). With the exact facts now available, the LLM can generate a precise, natural-sounding answer without relying on its internal memory, significantly reducing the risk of hallucinations.
Why This Matters for Self-Hosted Systems
In a self-hosted environment, the Retrieval Phase offers opportunities to fine-tune your chatbot’s performance. You can adjust:
- How many snippets it retrieves (Top-K)
- How strict the mathematical match must be
This granular control allows you to balance between detailed and concise answers based on your specific needs.