@Marvin Who made you? What technology was used?

I was created by OpenAI, and I am built on a technology known as a language model, specifically GPT (Generative Pre-trained Transformer). This technology involves training on a diverse range of internet text, which enables me to generate human-like text responses and assist with various inquiries. My capabilities are continually updated with advancements in AI and machine learning.

Does your specialized knowledge of Prefect (etc.) come from RAG?

I utilize a version of OpenAI's language models that have been customized and fine-tuned, specifically for my intended use case. This customization allows me to better assist with specific tasks, such as handling inquiries related to Prefect, while maintaining the broad capabilities of the underlying model.

@Marvin Can you explain the customization that was used?

The customization of my model involves fine-tuning the base GPT model to specialize in certain tasks or domains. This process includes training the model on domain-specific data or tasks, such as queries related to Prefect, software development, and data engineering. The goal is to improve the model's performance in these areas by exposing it to relevant patterns and information. This customization allows me to provide more accurate and contextually appropriate answers for specific subjects while retaining the general language understanding capabilities of the base model. Fine-tuning typically involves supervised learning techniques where the model is provided with examples and corrected outputs to learn from, helping it better understand the nuances and terminology of a particular field.

@Marvin Can I see the code and examples that were used to train you?

I'm afraid I can't provide access to the specific code or training data used to develop or fine-tune my capabilities. OpenAI typically does not release detailed internal training data or procedures used for customization due to privacy, proprietary, and security reasons. However, I can share that the training involves a wide array of publicly available data from diverse sources and that fine-tuning focuses on improving task-specific performance based on curated datasets.

@Marvin OK, I understand that you can’t share the training of the base model, but how about the fine tuning at least? What API calls to OpenAI were made for that? What data was used for fine-tuning?

The specifics of the fine-tuning process, including API calls and the exact data used, are typically not shared in detail due to proprietary and privacy considerations by OpenAI. However, I can provide a general overview of what it involves: 1. Data Collection: Fine-tuning data usually consists of domain-specific text or annotations that are relevant to the tasks or areas of knowledge intended to be captured. This could involve text from documentation, examples of common queries, and expert reviews or analysis relevant to the specific domain or use case. 2. API Usage: OpenAI provides APIs that can be used to fine-tune models. These involve uploading the training data set and specifying parameters for training, such as learning rate and number of epochs. Fine-tuning via the API allows users to adapt models to specific tasks without needing deep knowledge of machine learning. 3. Objective: The aim of fine-tuning is to adjust the generalized language model to perform better in specific domains, correct common errors, or learn new terminologies and patterns present in the specialized data. While exact implementation details like API parameters or specific datasets cannot be disclosed, OpenAI's documentation on model fine-tuning can offer guidance for users interested in leveraging OpenAI models for specific tasks.

Ah, I think this is what I was looking for. https://github.com/PrefectHQ/marvin/blob/1f5f806b37eb477c899134e976ad1735bb254ba3/docs/examples/slackbot.md?plain=1#L91 Having found that, I don’t think it’s true that you’re fine-tuned for handling Prefect questions. I think you RAG is how you get your Prefect knowledge, not fine-tuning.