Table of Contents

What is Query Contextualization?


TL;DR

Query contextualization is the technique of automatically reformulating a follow-up question in a conversation into a fully self-contained query before sending it to a retrieval system. In a multi-turn conversation, users naturally write terse follow-ups like "What about the second one?" or "How does that compare?". These questions are meaningless to a retrieval engine that has no memory of the conversation. Query contextualization solves this by using the LLM to rewrite the follow-up into an explicit, standalone query (e.g., "How does the performance of Model B compare to Model A on the MMLU benchmark?") so that embedding-based search retrieves the right documents. Without this step, RAG pipelines silently fail on multi-turn conversations because the retriever fetches irrelevant results. LM-Kit.NET implements query contextualization via QueryGenerationMode.Contextual on PdfChat and RagEngine, configurable through QueryContextualizationOptions.


What Exactly is Query Contextualization?

Consider a typical multi-turn conversation with a RAG system:

Turn 1:
  User:  "What are the main features of Qwen 3?"
  System: [retrieves documents about Qwen 3, generates response]

Turn 2:
  User:  "How does it compare to Gemma 3?"

At Turn 2, the retrieval system receives the query "How does it compare to Gemma 3?" This query contains a pronoun ("it") that refers to Qwen 3, but the retriever has no access to conversation history. It searches the vector store for documents matching "How does it compare to Gemma 3?" and likely returns generic comparison documents or documents about Gemma 3 alone, missing the Qwen 3 context entirely.

Query contextualization inserts a rewriting step before retrieval:

Without contextualization:
  User follow-up: "How does it compare to Gemma 3?"
       ↓
  [Retriever searches for "How does it compare to Gemma 3?"]
       ↓
  Poor results (missing Qwen 3 context)

With contextualization:
  User follow-up: "How does it compare to Gemma 3?"
       ↓
  [LLM rewrites using conversation history]
       ↓
  Rewritten query: "How does Qwen 3 compare to Gemma 3 in
  terms of main features and capabilities?"
       ↓
  [Retriever searches for the rewritten query]
       ↓
  Relevant results about both models

The key insight is that retrieval and generation have different context requirements. The LLM generating the final answer can see the full conversation, but the retriever only sees the current query. Contextualization bridges this gap by encoding the conversational context into the query itself.

Why Simple Approaches Fail

You might think you could solve this by simply concatenating the conversation history with the query. This does not work well for several reasons:

  • Noise: Long conversation histories dilute the query signal, causing the retriever to match on irrelevant parts of the history
  • Embedding distortion: Embedding models are optimized for single queries, not long conversational transcripts
  • Contradictions: Earlier parts of the conversation may contain outdated context that contradicts the current question
  • Performance: Embedding long concatenated strings is slower and produces lower-quality vectors

LLM-based rewriting is more effective because the model understands what information from the history is relevant to the current question and can produce a focused, self-contained query.


Why Query Contextualization Matters

  1. Multi-Turn RAG Accuracy: Without contextualization, RAG systems work well on first questions but degrade on follow-ups. Most real conversations involve multiple turns, so this problem affects the majority of user interactions.

  2. Pronoun and Reference Resolution: Users naturally use pronouns ("it", "they", "that one"), demonstratives ("this approach"), and ellipsis ("And the cost?"). These are natural in conversation but catastrophic for retrieval. Contextualization resolves all of these.

  3. Topic Continuity: When a user shifts topics within a conversation, contextualization detects which parts of the history are relevant to the current query and which should be ignored, producing a query that reflects the user's current intent.

  4. Transparent to the User: The rewriting happens automatically. Users interact naturally without needing to formulate each question as a standalone query. This is essential for good user experience in conversational AI systems.

  5. Complementary to Other Retrieval Improvements: Contextualization works alongside reranking, multi-query retrieval, and MMR diversity filtering. Each technique improves a different aspect of retrieval quality.


Technical Insights

How Contextualization Works

The contextualization process uses a lightweight LLM call before retrieval:

Input to the LLM:
  System: "Given the following conversation history and a follow-up
  question, rewrite the question to be a standalone query that
  captures all necessary context. Do not answer the question,
  only rewrite it."

  Conversation history:
    User: "What are the main features of Qwen 3?"
    Assistant: "Qwen 3 offers multilingual support, tool calling,
    extended context windows up to 128K tokens..."

  Follow-up question: "How does it compare to Gemma 3?"

Output from the LLM:
  "How do the main features of Qwen 3 (multilingual support,
  tool calling, 128K context) compare to those of Gemma 3?"

This rewritten query is then used for embedding-based retrieval, producing far more relevant results.

When to Use Contextualization

Scenario Contextualization Needed?
Single-turn Q&A No, each query is already standalone
Multi-turn chat with RAG Yes, follow-ups almost always need rewriting
Document Q&A with follow-ups Yes, users drill into documents conversationally
Agent with tools Sometimes, depends on whether retrieval tools receive raw user queries

Cost and Latency Considerations

Contextualization adds one LLM call per retrieval. This is typically fast because:

  • The rewriting prompt is short (just conversation history + follow-up)
  • The output is short (just the rewritten query)
  • A small, fast model can handle rewriting effectively

The latency cost (typically 100-500ms) is almost always worth the accuracy improvement. Without contextualization, incorrect retrieval leads to incorrect answers, which is far more costly than a brief delay.

Combining with Other Query Strategies

Contextualization can be combined with other RAG query strategies in a pipeline:

User follow-up
    ↓
[Contextualize] → Standalone query
    ↓
[Multi-Query] → Multiple query variants
    ↓
[Retrieve] → Results from each variant
    ↓
[RRF Merge] → Combined, deduplicated results
    ↓
[MMR Filter] → Diverse final results
    ↓
[Rerank] → Best results first
    ↓
[Generate] → Final answer

Each step improves a different aspect: contextualization fixes the query, multi-query retrieval improves recall, reciprocal rank fusion merges results, MMR ensures diversity, and reranking refines ordering.


Practical Use Cases

  • Conversational Document Q&A: Users chat with a knowledge base, asking follow-up questions that drill deeper into topics. Without contextualization, the second and subsequent questions often retrieve wrong documents. See Chat with PDF Documents.

  • Customer Support Bots: Support conversations are inherently multi-turn. A customer describes their problem, then follows up with "What if that doesn't work?" or "Can you try another approach?". Contextualization ensures each retrieval step finds relevant solutions.

  • Research Assistants: Researchers explore a corpus by asking a series of related questions. "What do the 2024 studies say?" followed by "And the earlier ones?" requires contextualization to resolve "earlier ones" to the correct time frame and topic.

  • Private Document Knowledge Bases: Enterprise RAG systems where employees ask multi-turn questions about internal documentation. See Build Private Document Q&A.


Key Terms

  • Query Contextualization: The process of rewriting a conversational follow-up question into a standalone query that encodes all necessary context from the conversation history.

  • Coreference Resolution: Resolving pronouns and references (e.g., "it", "that model") to their actual referents using conversation context.

  • Standalone Query: A query that contains all the information needed for retrieval without requiring access to any prior conversation history.

  • Query Rewriting: The general technique of transforming a user's query into a form more suitable for retrieval, of which contextualization is a specific type.

  • Conversational Retrieval: Retrieval in the context of multi-turn conversations, where each query may depend on prior turns.





External Resources


Summary

Query contextualization is the essential bridge between natural multi-turn conversation and effective RAG retrieval. Users write follow-up questions with pronouns, references, and implicit context that embedding-based retrievers cannot interpret. Contextualization uses the LLM to automatically rewrite these follow-ups into standalone queries that capture the full conversational context, ensuring that retrieval returns relevant documents at every turn. Without it, RAG accuracy degrades sharply after the first question. LM-Kit.NET supports this via QueryGenerationMode.Contextual on PdfChat and RagEngine, with fine-grained control through QueryContextualizationOptions. Combined with multi-query retrieval, MMR, and reranking, contextualization is one component of a robust retrieval pipeline that handles real conversational workloads.

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