Why is text-davinci-003 producing inconsistent and garbled JSON output?

Below is an analysis of potential issues and some recommended approaches:

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1. Relying on a Text Generation Model for Structured Output

The text-davinci-003 engine is optimized for general text generation. Although it can produce JSON-like text, it isn’t strictly enforcing JSON rules. This can lead to unexpected characters, smart quotes, extra spaces, and misformatted keys when multiple objects are generated. In other words, the model isn’t “built” to follow strict JSON formatting without additional guidance.

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2. Prompt Format Issues

Your prompt instructs to produce a JSON object with a specific structure. There are some things to watch for:

• Extra or Mismatched Brackets:
  Your prompt shows a format with extra closing brackets (}]}]). If the formatting in your prompt isn’t exact then the model may try to mimic the error.

• Verbosity in the Instruction:
  While you provide details for the properties, the model might “drift” after generating a few objects. Repetition or slight mistakes in the further parts of the prompt may compound those issues.

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3. Recommendations

a. Use JSON Mode / Structured Outputs

OpenAI now provides ways to tell the model to produce structured outputs. Instead of relying only on a text prompt, you can use the JSON mode (or structured outputs) options in the API call. For example:

JSONCopied
{
  "output_format": { 
    "type": "json_object" 
  },
  "prompt": "... your prompt here ..."
}

This helps ensure the output is valid JSON.

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b. Instruct the Model Clearly

Add a clear instruction such as:

IMPORTANT: The output must be a valid JSON array exactly matching the following format with no extraneous text:
[{"retQues": "string", "retAns": "string", "mcOptA": "string", "mcOptB": "string", "mcOptC": "string", "mcOptD": "string"}]

This reduces the chance of the model introducing smart quotes or splitting keys.

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c. Validate and Use Smaller Prompts

If possible, simplify the prompt. Instead of including unrelated context (e.g., the article text), consider using a simpler or standardized test case to see if the model complies. Once you get reliable results, you can generalize to larger inputs.

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d. Post-Processing vs. Guaranteed Consistency

Your PHP preg_replace approach is a workaround. Although cleaning the JSON is possible, each execution may require different fixes if the generation “drifts.” Instead, focus on refining the prompt or API parameters (such as using JSON mode) so that the model is forced to generate clean JSON without needing extensive post-processing.

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Summary

The core issues are due to:

• Using a text generation model (text-davinci-003) that isn’t optimized for generating strictly-structured JSON.
• Potential prompt formatting issues (extra/mismatched brackets or unclear instructions).

To resolve this, refine the prompt with explicit JSON-only instructions or use one of the newer API capabilities (structured outputs/JSON mode) designed to enforce valid JSON formatting. This should reduce or eliminate the garbled and inconsistent JSON output.

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By following these recommendations, you should see improved consistency in the model’s JSON output.