13 Oct Example of Retrieval Augmented Generation (RAG)

Let us now see an example displaying the process of RAG. This is an example flow displaying what happens when you ask a query or type a prompt.

Here is the flow:

1. Input Query: Let’s say our prompt is “Tell me about the history of the Taj Mahal.”
2. Document Retrieval:
   Initial Search: The system searches through a large dataset (like a collection of articles) to find relevant
   documents about the Taj Mahal.
   Chunking: These documents are divided into smaller chunks, each containing a few paragraphs or sentences.
3. Vector Embeddings:
   Encoding: The query “Tell me about the history of the Taj Mahal” is encoded into a vector. Each chunk of the retrieved documents is also encoded into vector embeddings.
4. Similarity Calculation:
   Similarity: The system calculates the cosine similarity between the query vector and each chunk’s vector.
   Selection: The top-k most relevant chunks are selected based on their similarity scores.
5. Relevance Ranking:
   The retrieved and encoded document chunks are ranked according to their relevance to the query using
   ranking algorithms.
6. Context Encoding: Encode the query and selected documents together.
7. Attention Mechanism: Focus on the most relevant parts of the context.
8. Response Generation: Using an LLM like GPT, generate a detailed response
9. Output: “The Taj Mahal is a breathtaking white-marble mausoleum located in Agra, India, along the
   southern bank of the Yamuna River”

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