Retrieval-Augmented Generation (RAG): Advanced Techniques to Optimize Query Understanding

Retrieval-Augmented Generation (RAG) has emerged as one of the most practical approaches to improve the factual accuracy and reliability of Large Language Models (LLMs). By augmenting LLMs with external knowledge retrieval from databases, documents, or the web, RAG bridges the gap between raw generative power and grounded, real-world knowledge.

But there’s a catch: we cannot control the quality of the user query.

Users may ask ambiguous, overly abstract, or overly specific questions. As a result, the retrieved context may be irrelevant, leading to weak or misleading responses.

This blog dives deep into advanced RAG query optimization techniques—from parallel query fan-out to query decomposition and HyDE—that improve accuracy and robustness of LLM-powered applications.

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🌐 The Foundations of RAG

Before jumping into advanced techniques, let’s recap the two core steps of RAG:

1. Chunking

   • Splitting large documents into smaller pieces (“chunks”).

   • Example: Breaking a 100-page legal contract into 500-word paragraphs.

   • Ensures retrieval works efficiently and relevant context fits within token limits.

from langchain.text_splitter import RecursiveCharacterTextSplitter

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=500,
    chunk_overlap=50
)

chunks = text_splitter.split_text(long_document)

2. Indexing

   • Converting chunks into vector embeddings for fast similarity search.

   • Typically done using vector databases like Pinecone, Weaviate, or FAISS.

from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_texts(chunks, embeddings)

With this base in place, we can explore how to improve query understanding before retrieval.

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⚡ Technique 1: Parallel Query (Fan-Out) Retrieval

Rewriting a single query into multiple variations to cover different angles.

Instead of depending on a single user query, we fan out by rewriting it into multiple semantically different queries. Each rewritten query is used for retrieval, and the results are merged before being passed to the LLM.

📊 Why it works: If the original query is ambiguous, one of the rewrites will likely hit the right context.

from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI

llm = OpenAI()

rewrite_prompt = PromptTemplate(
    input_variables=["query"],
    template="""
    Rewrite the following query in 5 different ways:
    Query: {query}
    """
)

rewrites = llm(rewrite_prompt.format(query="What is the impact of AI on jobs?"))
rewritten_queries = rewrites.split("\n")

Each rewritten query is sent to the vector DB:

docs = []
for rq in rewritten_queries:
    docs.extend(vectorstore.similarity_search(rq, k=3))

unique_docs = list({d.page_content: d for d in docs}.values())

✅ Pros: Greatly improves coverage for vague queries.

❌ Cons: May retrieve too many irrelevant docs due to query expansion.

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⚡ Technique 2: Reciprocal Rank Fusion (RRF)

Rank documents across multiple queries and merge based on frequency & position.

RRF improves on Fan-Out by ranking documents. Instead of dumping all retrieved results, it assigns scores based on repetition and position across different queries. Higher-ranked docs are prioritized for the LLM.

from collections import defaultdict

def reciprocal_rank_fusion(results, k=60):
    scores = defaultdict(float)
    for result_set in results:
        for rank, doc in enumerate(result_set):
            scores[doc.page_content] += 1 / (rank + k)
    return sorted(scores.items(), key=lambda x: x[1], reverse=True)

# Usage
all_results = [vectorstore.similarity_search(rq, k=5) for rq in rewritten_queries]
ranked_docs = reciprocal_rank_fusion(all_results)

✅ Pros: Balances recall and precision, reduces noise from irrelevant docs.

❌ Cons: Slightly more complex to implement than simple fan-out.

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⚡ Technique 3: Query Decomposition

Sometimes queries are too abstract (“Explain quantum computing”) or too detailed (“List all applications of GPT-4 in Indian law firms in 2025”).

We can handle this in two ways:

3.1 Making the Query Less Abstract

Break down an abstract query into smaller, answerable steps.

Example:

"Explain quantum computing" →

What is quantum computing?

How does it differ from classical computing?

What are real-world applications?

Implementation:

decompose_prompt = """
Break down the following query into smaller steps:
Query: {query}
"""

steps = llm(decompose_prompt.format(query="Explain quantum computing"))
steps_list = steps.split("\n")

context = ""
for step in steps_list:
    docs = vectorstore.similarity_search(step, k=3)
    step_answer = llm(f"Answer based on context: {docs}")
    context += step_answer + "\n"

Finally, combine step answers into a full response.

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3.2 Making the Query More Abstract

Sometimes, queries are too specific. In that case, we step back and rewrite them into a more generic query.

This is called Step-Back Prompting.

Original Question: "What are the applications of GPT-4 in Indian law firms in 2025?"
Stepback Question: "What are applications of GPT-4 in law firms?"

This improves retrieval by avoiding overfitting to a very specific detail.

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⚡ Technique 4: HyDE (Hypothetical Document Embedding)

HyDE asks the LLM to generate a hypothetical document answering the user’s query. That document is then embedded and used for retrieval.

📊 Why it works: If the user query is short or unclear, the generated doc acts as a richer query vector.

query = "Impact of blockchain on healthcare"

hypo_doc = llm(f"Write a short passage answering: {query}")

docs = vectorstore.similarity_search(hypo_doc, k=5)

✅ Pros: More accurate retrieval, especially for vague queries.

❌ Cons: Requires strong LLMs (e.g., GPT-4o) to generate useful hypothetical docs.

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⚡ Choosing the Right Technique

• When queries are vague → Use Fan-Out or HyDE.

• When precision is critical (legal, medical apps) → Use Reciprocal Rank Fusion.

• When queries are abstract → Use Query Decomposition (less abstract).

• When queries are overly detailed → Use Step-Back Abstraction.

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🔑 Key Takeaways

• RAG is only as good as the query understanding step.

• Different query optimization strategies work better for different use cases.

• Tradeoff: More accurate techniques (like decomposition) are often slower.

• Production-ready RAG systems often combine multiple techniques for best results.

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✨ Closing Thoughts

RAG isn’t just about retrieval—it’s about ensuring that the retrieved context is the right one. Optimizing user queries with these advanced techniques can significantly improve accuracy and trustworthiness in real-world applications, from chatbots to legal assistants to enterprise search tools.

If you’re building production-ready RAG apps, don’t just rely on vanilla similarity search—experiment with Fan-Out, RRF, Decomposition, and HyDE to get state-of-the-art results.