Returning an incorrect json response with single quotes in content

Hi Guys,
The api is returning a json with single quote and I am not sure how to fix this.


  "statusCode": 200,
  "body": {
    "id": "chatcmpl-7buSixJFD3mLGOasFKI8qT6SyAVgo",
    "object": "chat.completion",
    "created": 1689269532,
    "model": "gpt-4-0613",
    "choices": [
        "index": 0,
        "message": {
          "role": "assistant",
          "content": "{'error': ''}"

Can someone kindly help me figure this out?


Hi, what about the single quote is an issue for you?

If the strings are enclosed using single quotes, then the JSON is an invalid JSON .
Parser wont work

1 Like

There’s two things to specify for your function parameters, which can be added as a system prompt, or the description of function or parameter to correct an error regularly produced:

“JSON framework must be constructed with double-quotes. Double quotes within strings must be escaped with backslash, single quotes within strings will not be escaped.”

From my testing, if the JSON is in content, it will use single-quote. If the JSON is the result of function calling, it will use double-quote.

1 Like

I am not using functions. Its a bit of alchemy. I have played it around more.

In my prompt, I have something like this
The example of sample out put look like this {response_format}
The response_format loads from a json file which has all the name and values in double quotes in json

If I remove it I get double quotes, but a structure that is not to my liking. If I keep it, I get single quote in json

The model needs to see an example of the JSON you want back and that JSON should use double quotes. I would recommend only asking the model to return strings as it gets a little dicey if you ask it to return a mix of types likes numbers and bools.

The model does not need to see an example of the JSON.

It only needs specifications, ideally that tell it where it is positioned, within what software.

It is not dicey when asking for a mix of types; only strings need quotes on output value.

Prompting gpt-3.5-turbo counter to your advice (and example I just imagined, as we don’t have use case of @itsnomihere ):

1 Like

How do you work with slightly complex json structure, for inner names to be array?

Prompt used above, so you don’t have to retype to fix all your JSON woes:

# Instruction

You are a helpful but brief answering assistant for eight-year-old children, generating up to 40 word output only in JSON for our web site's python backend.

# Output format
JSON, keys and values require double-quotes
JSON keys: ["response", "topic grade level", "too_hard"]
JSON value type: [escaped string, int, truth value]

For a more complex json structure, your inner array, I had to inject a “[AI: JSON output format]” post-prompt to get it to not completely ignore the JSON instructions and just produce regular text. That solved, the system message works as anticipated:

# AI Rules 
- Output only described JSON output format compatible with python backend.
- JSON, keys and values require double-quotes

# Role
You are a helpful and brief answering assistant for eight-year-old children. Generate up to 40 word response.

# JSON output format

JSON keys: ["user input", "response", "grade_report"]
JSON value type: [escaped string, escaped string, JSON subarray 1]

JSON subarray 1: "grade_report"
JSON subarray 1 keys: ["user input estimated grade level", "AI output estimated grade level", "grade level required to answer", "question exceeds target level"]
JSON subarray 1 value type: [int, int, int, truth bool]


In a chat, the first response serves as a few-shot. Double quotes still.

1 Like

Sorry, not trying to be argumentative… the thing to understand is that these models are fundamentally pattern recognizers. They will always favor following patterns over instructions. To that end, showing them a pattern of the JSON to return will always out perform a schema definition of the JSON to return.

Showing them schema definitions CAN work but it’s not perfect. Honestly, nothing is perfect. The models are probabilistic so there’s always going to be a chance of error. I feel like I’ve gotten fairly close to making them as reliable as you can by confronting them when they make a mistake and simply asking them to correct their error. This works surprisingly well.

1 Like

True, and any and all techniques combined improve the quality of output that is now always on a knife’s edge of breaking.

However, the massive instruction-following training degrades the completion-example style multi-shot ability – and then they reduced instruction-following (disobeying jailbreakers by example).

My JSON above, we can put a few example user/assistant roles to improve its understanding of the actual JSON values expected, so it doesn’t just get the format right, it knows how to answer the questions inside also.

One now must be careful not to bias GPT with the examples given, because it sees the system prompt through a very blurry window: A 20-questions game with examples in the system prompt? It’s going to only ask those exact questions until you make them preposterous, like “does it live on the moon?”

Counterpoint: Davinci-003, (from the days of actual prompt engineering, where pure completion is purely example and leading it on), I show the quality to be destroyed in six months, where AI gets it right in one example (unlike turbo, its text length also correct):

Our website’s API AI answers like this:

Human: What is a math modulus function?
“user input”: “What is a math modulus function?”,
“response”: “A modulus function in math gives the remainder when one number is divided by another. For example, 7 divided by 3 is 2 with a remainder of 1, so the modulus of 7 and 3 is 1.”,
“grade_report”: {
“user input estimated grade level”: 4,
“AI output estimated grade level”: 6,
“grade level required to answer”: 5,
“question exceeds target level”: false
Human: What the relationship between e and log?
> AI:
> {
> “user input”: “What is the relationship between e and log?”,
> “response”: “The natural logarithm of a number is the power to which the base e (2.71828…) must be raised to equal that number. In other words, the natural logarithm of a number is the inverse of the exponential function of that number.”,
> “grade_report”: {
> “user input estimated grade level”: 4,
> “AI output estimated grade level”: 8,
> “grade level required to answer”: 7,
> “question exceeds target level”: false
> }
> }

1 Like