https://platform.openai.com/docs/guides/fine-tuning

You create a series of prompts and their responses and then that tuned model is used with that implicit knowledge already stored in it.

For example a notebook for "lets train GPT on the information about the olympics - https://github.com/openai/openai-cookbook/blob/main/examples... and https://github.com/openai/openai-cookbook/blob/main/examples... and https://github.com/openai/openai-cookbook/blob/main/examples... )

The gotcha for this is that while regular Davinci is $0.02/1k tokens, training is $0.03/1k tokens and use is $0.12/1k tokens.

The other thing to consider is that Chat GPT has a session and history for that session. You can use GPT stateless which doesn't have the "it gets confused about what you were talking about before."

    curl https://api.openai.com/v1/completions \
      -H "Content-Type: application/json" \
      -H "Authorization: Bearer $OPENAI_API_KEY" \
      -d '{
      "model": "text-davinci-003",
      "prompt": "Write a recipe based on these ingredients and instructions:\n\nFrito Pie\n\nIngredients:\nFritos\nChili\nShredded cheddar cheese\nSweet white or red onions, diced small\nSour cream\n\nInstructions:",
      "temperature": 0.3,
      "max_tokens": 120,
      "top_p": 1,
      "frequency_penalty": 0,
      "presence_penalty": 0
    }'

And thus asking it about one and only one thing with no additional chat context around it.