Retrieving Relevant Information
Retrieval is the process of finding the most relevant document chunks for a user's question. Good retrieval is essential for a high-quality RAG system.
8 min•By Priygop Team•Updated 2026
The Retrieval Process
- Step 1: The user's question is converted to an embedding using the same model used for documents
- Step 2: The embedding database (vector store) is searched for the closest matches
- Step 3: The top N most similar chunks are retrieved (typically 3-10 chunks)
- Step 4: The retrieved chunks are formatted and inserted into the AI's prompt as context
- Step 5: The AI model generates its answer based on both the question and the retrieved context
Diagram
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Deep Learning ⊂ Machine Learning ⊂ Artificial Intelligence
Retrieval Best Practices
- Retrieve 3-5 chunks for most use cases: more is not always better
- Check that retrieved chunks are actually relevant before sending to the model
- Keep chunk sizes reasonable (200-500 words): too small loses context, too large is noisy
- Include chunk metadata (document title, page number) so the AI can cite its sources
- Use hybrid search: combine semantic (embedding) search with keyword search for best results
Key Takeaways
- Retrieval is the process of finding the most relevant document chunks for a user's question.
- Step 1: The user's question is converted to an embedding using the same model used for documents
- Step 2: The embedding database (vector store) is searched for the closest matches
- Step 3: The top N most similar chunks are retrieved (typically 3-10 chunks)