Why do custom GPTs and Assistants behave differently with identical files and instructions?

Below is a summary of key considerations that can help explain the differences you’re seeing between your custom GPTs and Assistants:

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1. Why Is This Happening?

• Different Underlying Implementations:
  Custom GPTs (configured via the “knowledge” system) and Assistants use different design paradigms. Custom GPTs tend to incorporate their attached files into a more tightly integrated context (akin to fine-tuning) which improves their ability to reference that material. In contrast, the Assistants API is built to handle dynamic conversations and open-ended interactions, which means that its internal handling of attached files may be less deterministic or tightly coupled to the content.

• Contextual Handling & Memory:
  The custom GPT configuration often “caches” or otherwise leverages its knowledge base more directly. Assistants, on the other hand, dynamically parse the conversation—which may result in challenges if the assistant is not consistently retrieving or applying the attached file content. This difference can lead to situations where the Assistants either omit or inadequately reference the available FAQs and supporting documentation.

• Safety and Response Guardrails:
  Some behaviors (including the hallucination issues you mentioned) can be a result of built-in safety measures designed for Assistants. These guardrails sometimes lead to the assistant declining to answer or providing overly conservative responses when the context isn’t explicitly prompting a safe answer.

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2. Will This Be Fixed?

• Under Continuous Improvement:
  The Assistants API is still evolving—currently in beta—and the engineering teams are aware of these kinds of challenges. You might expect improvements over time as the platform iterates on context handling, file integration, and overall retrieval effectiveness.
• Intentional Differences:
  It does appear that certain design choices (whether for safety or dynamic conversation flexibility) lead to less reliable file referencing compared with custom GPTs. This isn’t necessarily “broken” behavior, but rather a consequence of different product goals and architectures. Future updates may narrow this performance gap, but no specific timeline has been provided.

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3. Difference Between “Files” for Assistants and “Knowledge” for GPTs

• Custom GPT “Knowledge”:
  In custom GPT configurations, knowledge materials are prepared as part of the model’s setup. This system is designed to integrate the provided files more closely with the model’s responses, almost as if the model were fine-tuned on that material.

• Assistant “Files”:
  With Assistants, the attachment of files is done in a different way. Although the files offer context and references for the conversation, the assistant uses a dynamic reasoning process (e.g., assembling responses in a defined JSON format) which means it might not always extract or prioritize the content from those files in the same way.

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4. Guidance Material for Improving Assistant Performance

• Reviewing Documentation:
  Developers are encouraged to review the technical documentation for the Assistants API. There are guides that illustrate how to tailor the integration (for example, by fine-tuning instructions or modifying how files are interpreted). This documentation covers:
  • How to set up and modify instructions.
  • Techniques for optimizing file usage within your conversational application.
• Adjusting Instructions:
  It is important to experiment with the instruction set provided to the assistant. Some developers have found that more explicit instructions regarding file usage, retrieval of FAQs, or formatting expectations can lead to improved performance.

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5. Word Counts and File Formats

• Effective Content Integration:
  The size and formatting of files can have an impact. Large files or those with non-standard formatting may not be processed as effectively.
• Optimizing for Context Windows:
  Consider chunking or summarizing extensive documents. Files that are concise and formatted in a way that the assistant can easily parse (such as well-structured Markdown) are more likely to be effectively used as a knowledge base within the available token context.

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In Summary

• The observed differences stem primarily from the two systems’ architectures—custom GPTs integrate “knowledge” in a way that boosts precise referencing, while Assistants balance open-ended conversation with safety and dynamic reasoning.
• The product teams are actively gathering feedback, and as the Assistants API is still in beta, further improvements and refinements may address these challenges over time.
• It’s useful to revisit the documentation regarding both file integrations and conversational instructions. Tweaking your assistant’s setup (including clarifying instructions and optimizing file formatting) may help close the performance gap you’re experiencing.

These insights are derived from current technical guidelines and product overviews. As the platform evolves, keep an eye on updates in our documentation for the latest best practices and feature improvements.