Using foundational texts for improving responses


I’m new in the community, and very happy to be part of this huge project. I’m also very appreciative of all the amazing work thousand of developers have done.

I am creating a custom gpt in GPTs. It applies a special logic called the dynamic logic of the contradictory, which you’d surprised how easy it is to do. This GPT investigate as a scientist different phenomena.

At first I was experimenting alone without knowing there was this huge forum in the openAI site, so I asked GPT how could I create knowledge and it replied by creating foundational texts. However upon looking on the Internet and in this forum the term “foundational text” is not common at all… but that’s what I’ve been doing for the past weeks.

A foundational text is a text created from a reference text that helps the AI to answer more efficiently. For instance if I say “Create a foundational text on modern technologies, highlighting how AI could revolutionize economic models.”, it will create a text which then I upload to the Knowledge database.

I don’t know if I’m doing this correctly but it seems to work.

The reason I’m writing here it’s because I have a confusion regarding “training texts”, “foundational texts”, “training data” and “fine tuning”… could someone enlighten me in what category falls the approach I’ve been adopting?

Thanks for you time, have a nice day!

Hello, I think I was able to respond using GPT, but as I’m not an expert I’m not 100% sure, but it seems like a good response:

It sounds like you’ve been using a rule-based system or a set of predefined protocols to guide the generation of responses, rather than modifying the underlying model through fine-tuning. Both approaches have their merits and can be useful depending on the application. Here’s a comparison and some insights on both methods:

Rule-Based Systems (Response Protocols)

  1. How It Works:

    • You define a set of rules, templates, or protocols that the system follows to generate responses.
    • These rules can be based on keywords, patterns, or specific input structures.
  2. Advantages:

    • Control: You have precise control over the responses since you define the rules.
    • Predictability: Responses are consistent and predictable, adhering strictly to the protocols.
    • Simplicity: Easier to implement for specific, narrow use cases where the variability of responses is low.
  3. Disadvantages:

    • Scalability: Difficult to scale and maintain as the number of rules and possible inputs increases.
    • Flexibility: Less flexible in handling unanticipated inputs or variations in language.
    • Complexity: Managing and updating the rule sets can become complex and cumbersome over time.

Fine-Tuning Language Models

  1. How It Works:

    • You provide a dataset of examples that the model uses to learn and adjust its responses.
    • The model internalizes the patterns and nuances from the dataset, modifying its parameters to generate desired responses.
  2. Advantages:

    • Flexibility: Can handle a wide range of inputs and generate appropriate responses without explicit rules.
    • Scalability: Easier to scale as the model learns from examples, reducing the need for extensive rule management.
    • Natural Language Understanding: Better at understanding and generating natural language responses that are more human-like.
  3. Disadvantages:

    • Initial Effort: Requires an initial effort to prepare a comprehensive and high-quality training dataset.
    • Resources: Fine-tuning can be resource-intensive, requiring computational power and time.
    • Complexity: Understanding and implementing fine-tuning may require more technical expertise.

Combining Approaches

For many applications, a hybrid approach can be beneficial. Here’s how you might combine both methods:

  1. Initial Rule-Based System: Use your response protocols system to handle straightforward and predictable queries. This ensures that critical information is always delivered correctly.
  2. Model Fine-Tuning for Flexibility: Fine-tune a language model to handle more complex and variable queries. This can cover the gaps and handle cases where the rule-based system may fall short.
  3. Fallback Mechanism: Implement a fallback mechanism where the model falls back on predefined responses or protocols if it is unsure or if the confidence level is low.

Practical Steps for Transition

  1. Define Objectives: Clearly outline what you want to achieve with fine-tuning that goes beyond the capabilities of your rule-based system.
  2. Prepare Dataset: Gather and prepare a dataset of input-output pairs that represent the desired interactions. Include examples that are well-handled by your rule-based system and those that are not.
  3. Fine-Tuning: Use the prepared dataset to fine-tune the model. Ensure that the dataset is diverse and representative of the types of queries you expect.
  4. Testing and Validation: Test the fine-tuned model extensively to ensure it meets your requirements. Compare its performance with your rule-based system.
  5. Integration: Integrate the fine-tuned model with your existing system. Use the model for general queries and fall back on the rule-based system for specific, critical responses.

By combining the strengths of both approaches, you can create a more robust, flexible, and scalable system that delivers high-quality responses across a wide range of scenarios.