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Welcome to advanced RAG techniques! In this lesson, we'll explore two powerful methods to make your Retrieval Augmented Generation (RAG) system even smarter: Query Rewriting and Reranking.

These techniques help ensure your LLM gets the most relevant information possible, leading to better and more accurate responses.

When a user asks a question, their initial query might not be perfect for searching your knowledge base. It could be:

• Too short or vague: Lacking specific keywords.
• Ambiguous: Having multiple possible meanings.
• Missing synonyms: Not using the exact terms found in your documents.

This can lead to your retriever fetching less relevant documents.

Query rewriting is the process of modifying the user's original query before it's sent to your document retriever.

The goal is to transform the query into a more effective search term that is more likely to match relevant documents in your vector database.

Query rewriting can involve several strategies:

• Query Expansion: Adding synonyms or related terms to broaden the search.
• Query Rephrasing: Changing the query's structure or wording to improve clarity.
• Query Decomposition: Breaking a complex, multi-part query into simpler, individual sub-queries.

Often, another LLM is used to perform these rewriting tasks.

Here's a conceptual Python example of how a simple query expansion might work. In a real system, an LLM would do the heavy lifting.

Even after a great initial search (perhaps with a rewritten query!), the top 'N' documents returned by your retriever might not be perfectly ordered by relevance.

The retriever's job is often to find potential matches. Reranking steps in to refine this order, ensuring the absolute best documents are at the very top.

Reranking works like this:

1. Your initial retriever fetches a larger set of candidate documents (e.g., top 50).
2. A specialized reranker model then takes each of these candidate documents, along with the original user query, and provides a more precise relevance score.
3. The documents are then sorted again based on these new, more accurate scores.

This ensures the most relevant documents are passed to the LLM.

Unlike a retriever that often uses embeddings for approximate similarity, rerankers typically use more sophisticated models, often called cross-encoders.

• Cross-encoders take both the query AND a document as input.
• They consider the interaction between the query and document terms directly.
• This allows for a much more nuanced understanding of relevance, though it's computationally more intensive, hence why it's only applied to a smaller subset of documents.

This example shows how a reranker might re-score and reorder an initially retrieved list of documents based on their relevance to the query.

The true power comes from combining both techniques:

• First, Query Rewriting creates a better search query.
• Then, your retriever uses this improved query to fetch a broader, more relevant set of documents.
• Finally, Reranking fine-tunes the order of these documents, ensuring the LLM receives the absolute best context to generate a response.

This multi-stage approach significantly boosts the quality and accuracy of your RAG system.

Time to test what you've learned about optimizing RAG through query rewriting and reranking.

Great job! In this lesson, you learned about Query Rewriting and Reranking.

• Query Rewriting modifies the user's input to create a more effective search query.
• Reranking reorders initially retrieved documents using a more precise model to surface the most relevant ones.

Together, these techniques significantly enhance the quality and accuracy of your RAG applications. Next, we'll explore even more advanced RAG architectures!