Effective Prompt Strategies for RAG Handling User Intent and Document Comparisons

Below is an outline of strategies and techniques used in more advanced RAG implementations—particularly when it comes to detecting user intent and handling multiple documents—that align with the concepts described in the LangChain documentation and related Assistant UI integrations:

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Handling User Intent Changes

When a user follows up by asking for a transformation (e.g., “shorten that answer”), it’s usually best to separate the retrieval step from the post‐processing. In practice, you can:

• Design layered chains:
  • First, run a RAG retrieval chain only for queries that need external context.
  • Then, if the query is a follow‐up request (like “make your previous answer shorter”), use a separate transformation or summarization chain that only manipulates the existing response rather than triggering a new vector search.

• Use conversation history:
  • Maintain chat history and use it to detect whether a new retrieval is needed or if the user is referring back to a previous result.
  • In LangChain, you can incorporate previous messages into your prompt so that the intent (asking for a change rather than a fresh answer) is clear.

Example (simplified):

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# First chain: retrieval + answer generation
response = rag_chain.invoke({"input": "What is Task Decomposition?"})

# Second chain: transformation
if user_message.lower().startswith("shorten"):
    new_input = f"Shorten the following answer: {response['answer']}"
    # Use a simple transformation chain (or a summarization chain)
    shortened_response = llm.invoke([HumanMessage(content=new_input)])

This separation avoids an unnecessary vector search when the intent is to modify the output.

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Handling Multiple Documents & Contrasting Information

When your retriever returns several documents, you may need to decide whether to:

• Aggregate all documents:
  For general answer generation, many RAG pipelines use techniques like “stuffing” where the retrieved documents are concatenated into a single context string. This is usually done with a prompt similar to:

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  system_prompt = (
      "You are an assistant for question-answering tasks. "
      "Use the following pieces of retrieved context to answer the question. "
      "If you don't know the answer, say that you don't know. "
      "Use three sentences maximum and keep the answer concise.\n\n"
      "Context: {context}"
  )

• Select or compare specific documents:
  When a user wants to contrast two documents, you can adjust your prompt instructions to encourage the model to discuss differences:

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  system_prompt_contrast = (
      "You are an assistant for analytical tasks. "
      "Compare and contrast the following two documents. "
      "Highlight the main differences between them in a clear and concise manner.\n\n"
      "Document A: {doc_a}\n\n"
      "Document B: {doc_b}"
  )

  In your retrieval logic, you might then split the documents according to relevance or allow the user to specify which documents to compare.

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Capturing and Inspecting Prompts Sent to the Assistants API

Currently, the backend flow that composes and sends prompts (e.g., via the LangChain chain or the Assistant UI integration with the Vercel AI SDK) does not automatically expose a history of the built prompts. However, you can capture or log them by:

• Explicitly formatting and logging prompts:
  Before invoking your LLM, format the prompt and send it to your logging system. For example:

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  # Format the prompt with the retrieved context
  system_prompt_fmt = system_prompt.format(context=docs_text)
  
  # Log the prompt for debugging
  print("Sending prompt to LLM:", system_prompt_fmt)
  
  # Then, proceed to invoke the chain or directly pass the messages to the API
  response = model.invoke([SystemMessage(content=system_prompt_fmt), HumanMessage(content=question)])

• Extending existing callbacks or hooks:
  With LangChain you can also use callbacks to inspect the entire chain process, capturing inputs and outputs at various steps. This technique can help you debug the final prompt that’s being sent.

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Summary

• User Intent Detection: Separate the retrieval and post-processing steps. Use conversation history and layered chains to detect whether a new search is necessary.
• Handling Multiple Documents: Either aggregate retrieved documents or design specialized prompts (such as for contrasting) to let the model know which approach to take.
• Prompt Logging: Modify your backend or chain logic to log the formatted prompt before sending it to the LLM. This can be done with straightforward print statements or by integrating callback hooks in LangChain.

These strategies are often implemented incrementally—start with a basic RAG pipeline and then add more fine-grained intent detection or logging capabilities as needed. Each project might require slight tweaks in the prompt templates to best guide the underlying model’s behavior.