Query Rewriting and Reranking
Explore techniques to optimize user queries and rerank retrieved documents for higher relevance to the LLM.
Query Rewriting and Reranking is a free LLM Apps in Production (RAG + Vector DB + Caching) lesson on CoddyKit — lesson 1 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the LLM Apps in Production (RAG + Vector DB + Caching) learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Optimizing Queries for RAG
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.
Why Raw Queries Fall Short
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.
Understanding Query Rewriting
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.
Techniques for Rewriting Queries
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.
Code: Simple Query Rewriting
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.
class QueryRewriter:
def rewrite(self, query):
# Simulate an LLM or a rule-based system
if "LLM performance" in query:
return query + " large language model efficiency optimization"
if "vector db" in query:
return query + " vector database semantic search"
return query
rewriter = QueryRewriter()
user_query_1 = "improve LLM performance"
rewritten_1 = rewriter.rewrite(user_query_1)
print(f"Original 1: {user_query_1}")
print(f"Rewritten 1: {rewritten_1}\n")
user_query_2 = "how to use vector db"
rewritten_2 = rewriter.rewrite(user_query_2)
print(f"Original 2: {user_query_2}")
print(f"Rewritten 2: {rewritten_2}")Why We Need Reranking
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.
The Reranking Process
Reranking works like this:
- Your initial retriever fetches a larger set of candidate documents (e.g., top 50).
- A specialized reranker model then takes each of these candidate documents, along with the original user query, and provides a more precise relevance score.
- The documents are then sorted again based on these new, more accurate scores.
This ensures the most relevant documents are passed to the LLM.
Specialized Reranking Models
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.
Code: Simulating Reranking
This example shows how a reranker might re-score and reorder an initially retrieved list of documents based on their relevance to the query.
class Reranker:
def rerank(self, query, documents):
# Simulate a cross-encoder model scoring documents
scores = {}
for doc in documents:
if "vector database" in doc.lower() and "fast" in query.lower():
scores[doc] = 0.95 # Highly relevant
elif "llm" in doc.lower() and "improve" in query.lower():
scores[doc] = 0.85
elif "database" in doc.lower():
scores[doc] = 0.7
else:
scores[doc] = 0.3 # Less relevant
# Sort documents by score in descending order
sorted_docs = sorted(documents, key=lambda d: scores.get(d, 0), reverse=True)
return sorted_docs
reranker = Reranker()
user_query = "How to build a fast vector database?"
initial_docs = [
"Introduction to LLMs",
"Building a scalable vector database",
"Optimizing LLM inference",
"Fast data ingestion for databases"
]
reranked_docs = reranker.rerank(user_query, initial_docs)
print(f"Original documents: {initial_docs}\n")
print("Reranked documents (most relevant first):")
for doc in reranked_docs:
print(f"- {doc}")The Power of Combination
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.
Check Your Understanding
Time to test what you've learned about optimizing RAG through query rewriting and reranking.
Recap & Next Steps
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!
Frequently asked questions
Is the “Query Rewriting and Reranking” lesson free?
Yes — the full text of “Query Rewriting and Reranking” is free to read here on the web, and the LLM Apps in Production (RAG + Vector DB + Caching) course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the LLM Apps in Production (RAG + Vector DB + Caching) course, upgrade to CoddyKit PRO.
What will I learn in “Query Rewriting and Reranking”?
Explore techniques to optimize user queries and rerank retrieved documents for higher relevance to the LLM. You practise LLM Apps in Production (RAG + Vector DB + Caching) with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start LLM Apps in Production (RAG + Vector DB + Caching)?
No prior experience is required. LLM Apps in Production (RAG + Vector DB + Caching) on CoddyKit is structured for beginners through advanced learners; this is — lesson 1 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Query Rewriting and Reranking” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this LLM Apps in Production (RAG + Vector DB + Caching) lesson?
Yes. Every LLM Apps in Production (RAG + Vector DB + Caching) lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Query Rewriting and Reranking
- Multi-stage and Agentic RAG Patterns
- Handling Complex Document Structures
- Self-Querying & Citations