https://platform.openai.com/docs/guides/fine-tuning https://platform.openai.com/docs/guides/fine-tuning You create a series of prompts and their responses and

https://platform.openai.com/docs/guides/fine-tuning 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/fine-tuned_qa/olympics-1-collect-data.ipynb https://github.com/openai/openai-cookbook/blob/main/examples... and https://github.com/openai/openai-cookbook/blob/main/examples/fine-tuned_qa/olympics-2-create-qa.ipynb https://github.com/openai/openai-cookbook/blob/main/examples... and https://github.com/openai/openai-cookbook/blob/main/examples/fine-tuned_qa/olympics-3-train-qa.ipynb 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.