Prompt Optimization - 302.AI API Document

Interface responses are consistent with OpenAI chat model responses, supporting streaming returns.

Optimization methods list:

ID	Name	Tip
1	CRISPE	Optimized according to the LangGPT framework, breaking down simple tasks into complex workflows and generating structured prompts.
2	CO-STAR	Singapore Prompt Competition champion framework, which breaks down, analyzes, and generates structured prompts for simple tasks with one click.
3	Meta Prompting	Prompt optimization method proposed by Tsinghua University and Shanghai AI Laboratory.
4	CoT	Improves the quality and relevance of model-generated content by simulating the thought process of problem-solving.
5	VARI	Google's Deepmind latest research, enhancing prompts through variational planning.
6	Q*	Optimizes prompts using Markov decision processes.
7	RISE	Carnegie Mellon University's latest research, allowing prompts to introspect recursively.
8	O1-STYLE	Mimics O1 by following structured thinking, step-by-step reasoning, continuous reflection, and adjustment strategies in prompts.
9	MicrOptimization	Microsoft's latest research, enhancing your prompt capabilities by automatically optimizing your instruction dataset.
10	OpenAI	OpenAI's officially open-sourced prompt optimization method.
11	claude	Claude's officially open-sourced prompt optimization method.
12	DRAW	Breaks down user input descriptions into six elements: shot, lighting, subject, background, style, and atmosphere, to supplement and refine, generating high-quality drawing prompts.
13	Complete Guide	Manually optimize prompts step by step.
14	modify	Manually optimize prompts.

Price: Calculated based on the model tokens used

Request Code Samples

Shell

JavaScript

Java

Swift

PHP

Python

HTTP

Objective-C

Ruby

OCaml

Dart

Responses

🟢200成功

application/json

Body

Example

{
    "id": "string",
    "object": "string",
    "created": 0,
    "model": "string",
    "choices": [
        {
            "index": 0,
            "message": {
                "role": "string",
                "content": "string",
                "refusal": null,
                "annotations": [
                    "string"
                ]
            },
            "logprobs": null,
            "finish_reason": "string"
        }
    ],
    "usage": {
        "prompt_tokens": 0,
        "completion_tokens": 0,
        "total_tokens": 0,
        "prompt_tokens_details": {
            "cached_tokens": 0,
            "audio_tokens": 0
        },
        "completion_tokens_details": {
            "reasoning_tokens": 0,
            "audio_tokens": 0,
            "accepted_prediction_tokens": 0,
            "rejected_prediction_tokens": 0
        }
    },
    "service_tier": "string",
    "system_fingerprint": "string"
}