Gpt-4 model be more specific in answers


I am developing a chat system that uses gpt-4 so that it can answer questions based on a pdf or document, now I need it to be more precise with the answers, since it gives me an answer from the pdf but not specifically what I am asking

How can I perfect it?

Thank you

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Improving the precision of answers generated by GPT-4 for questions based on a PDF or document can be challenging but is achievable with the right approach. Here are some steps you can take to make the responses more specific:

  1. Provide Clear and Specific Questions: When asking questions, make them as clear and specific as possible. Instead of asking open-ended or vague questions, frame your queries in a way that leaves less room for ambiguity. This can help the model generate more precise answers.
  2. Use Contextual Information: If the document or PDF has headings, subheadings, or section titles, refer to them in your questions. For example, you can ask, “In section 2.3 of the document, what is the key finding?” Providing context can guide the model to the relevant parts of the document.
  3. Specify Constraints: Clearly specify any constraints or conditions related to your question. For instance, you can say, “Limit your answer to information from the first three pages of the document.” This narrows down the scope and encourages a more specific response.
  4. Ask for Summaries: Instead of asking for direct answers, request the model to provide summaries of specific sections or paragraphs from the document. Summaries tend to be more concise and specific.
  5. Use Follow-up Questions: If the initial response is not specific enough, you can ask follow-up questions to seek clarification or request additional details. This iterative approach can help refine the answers over the course of the conversation.
  6. Evaluate and Refine: After receiving responses, evaluate their accuracy and specificity. If necessary, rephrase or reframe your questions based on the quality of the answers you receive. Keep iterating to improve the results.
  7. Leverage Keywords: Include relevant keywords from the document in your questions. This can signal to the model what specific information you’re looking for.
  8. Post-processing: After receiving responses, you can implement post-processing techniques to extract specific information or keywords from the model’s output. This can help you refine the answer further.
  9. User Feedback: Encourage users to provide feedback on the quality of the responses. This feedback can be valuable for making adjustments and improvements over time.

Remember that while GPT-4 is a powerful language model, it may still generate answers that require human validation and correction, especially when dealing with specific and complex document-related queries. Incorporating a manual review step can help ensure the accuracy of the responses generated by the model.