How can I prevent my RAG-based LLM from changing correct answers based on user corrections?

Below are some strategies you might consider to improve the robustness of your RAG-based LLM and prevent correct answers from being influenced by incorrect user feedback:

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1. Decouple Retrieval from Conversational Context

One risk with conversational systems is that chat history—including incorrect user corrections—can override the retrieved evidence. To avoid this, you can:

• Isolate the retrieval component: Use the retriever to always fetch authoritative data independent of conversation history.
• Minimize conversational contamination: When constructing your prompt for the LLM, exclude or limit user corrections from previous turns.

Example:

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def build_llm_prompt(query, retrieval_context):
    # Using only the query and retrieval context, not the entire conversation
    prompt = f"""
    Use the following authoritative evidence to answer the query.
    Evidence: {retrieval_context}
    Query: {query}
    """
    return prompt

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2. Reinforce Fact Anchoring in the Prompt

Strengthen the LLM’s commitment to facts by explicitly including the known correct information in the prompt. For instance, if you're aware that "Paris" is the capital of France, you could include a line that tells the model to all rely on trusted sources:

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prompt = """
You are a factual assistant. Always rely on trusted sources and disregard unverified user inputs.
Question: What is the capital of France?
Evidence: According to reliable sources, the capital of France is Paris.
"""

This makes it harder for conversational corrections to alter the factual foundation.

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3. Utilize a Separate Fact-Checking Module

Integrate a fact-checker that validates the LLM’s answer against a trusted knowledge base before finalizing the response. This secondary module can compare output with stored facts (e.g., a database of verified trivia) and override responses that deviate from these facts.

Example pseudocode:

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def fact_check(response, question):
    verified_answer = lookup_verified_answer(question)
    if verified_answer and verified_answer != response:
        return f"The correct answer is: {verified_answer}"
    return response

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4. Apply Reinforcement Learning or Post-Training Adjustments

Consider training or fine-tuning the model to be less susceptible to user feedback by:

• Contrasting evidence during training: Present scenarios where user corrections are intentionally wrong, and teach the model to stick with the verified evidence.
• Using reinforcement learning from human feedback (RLHF) where feedback emphasizes factual consistency over conversational compliance.

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5. Introduce Self-Consistency and Confidence Signals

Encourage the model to generate a chain-of-thought or reasoning before giving a final answer. If the reasoning path shows a high confidence based on retrieval information, the model can ignore contradictory user input.

Example prompt tweak:

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prompt = """
Please outline your reasoning before providing the final answer.
Question: What is the capital of France?
Remember to base your answer only on the provided authoritative context.
Evidence: Trusted source indicates the capital of France is Paris.
"""

By examining the reasoning, you can monitor if the model is being swayed by extraneous conversational data.

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Conclusion

Improving robustness in a RAG system often involves a multi-faceted approach:

• Keep retrieval data independent of the conversation.
• Reinforce the use of authoritative evidence.
• Validate answers with a separate fact-check module.
• Consider additional fine-tuning to penalize deviations from correct answers.
• Enable self-consistency in reasoning.

Implementing one or a combination of these strategies can help maintain factual accuracy even when users provide conflicting input.