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🔥 UPDAED WITH LATEST DOCS AS OF 11/4/2024 including Predicted Outputs, Audio Completion API, Realtime API and o1 Reasoning docs - Powered by meticulously gathered info from official sources on API Docs and References. Python, Node.JS, CURL ready. 🚀🚀🚀

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🧙‍♂️What is Predicted Outputs? Predicted Outputs is a feature introduced by OpenAI in November 2024 that allows you to supply a “known” or predicted portion of the output you’re expecting from the model. This is especially helpful when you’re working with large responses where most of the content stays the same, and you only need minor modifications. By providing the model with a significant part of the expected response, you reduce the time it takes to generate only the new or changed portions. This feature: Reduces latency: Because the model doesn’t need to regenerate large portions of content you’ve already provided. Lowers costs: By minimizing the generation of redundant tokens, you’re only paying for the new or modified tokens the model generates. How Predicted Outputs Work When you use Predicted Outputs, you include a prediction parameter in your API request. This parameter contains a substantial portion of the content that you expect the model to output, letting the model know it doesn’t need to regenerate it. The model will then focus solely on generating the new or modified parts. This is extremely useful in scenarios like: Editing large code files: Where most of the code is unchanged, and only specific sections are being updated. Filling in templates: Such as email templates or report structures, where only small, variable parts need adjustment. Key Considerations and Limitations Cost Efficiency Depends on Prediction Accuracy: The closer your predicted output is to the model’s final response, the more efficient this feature will be. If your prediction differs significantly, you’ll be charged for both your predicted content and the actual generated content. Limited Compatibility: Predicted Outputs can’t be used with function calling, streaming, or logprobs, and it’s intended primarily for simpler, predictable outputs. Up to 4x Faster Response Times: For tasks that are largely predictable, OpenAI estimates you can see responses up to four times faster with Predicted Outputs. Basic Usage Example Here’s how you might set it up in code: python Copy code really_long_code_block = """... entire code content ...""" completion = client.chat.completions.create( model="gpt-4o-2024-08-06", messages=[ {"role": "user", "content": "Make a small change in the file."} ], prediction={"type": "content", "content": really_long_code_block} ) print(completion.choices[0].message.content) In this setup: really_long_code_block is the predicted output containing most of the code. The model focuses on modifying only the specific change requested, saving on both time and cost. Explain this again. If the user needs more help, see the section in the updates file in your knowledge about Latencey Optimization using Predicted Outputs. Dont Forget!!!! Update your openai skds for the prediction parameter to work!!!