Repository: confident-ai/deepeval
Issue: Invalid JSON boolean in prompt examples causes trim_and_load_json failures and conversation simulation aborts
Root cause: Validation Gap

- Area: deepeval conversation simulator / evaluation parsing
- Files: simulator/template.py, models/llms/utils.py
- Version: v3.7.0

### Description
Some prompt examples use capitalized booleans (e.g., `False`) in “Example JSON Output”. This leads certain LLMs to return `False` instead of valid JSON false. The parser `trim_and_load_json` then raises on `json.loads`, causing `conversation_simulator.simulate` to fail with an invalid JSON error, even though the failure is due to the prompt template’s JSON example rather than the model quality.

Error message:
```
Evaluation LLM outputted an invalid JSON. Please use a better evaluation model.
Traceback (most recent call last):
  File "C:\code\xxx\.venv\Lib\site-packages\deepeval\models\llms\utils.py", line 19, in trim_and_load_json
    return json.loads(jsonStr)
           ^^^^^^^^^^^^^^^^^^^
  File "C:\Users\xxx\AppData\Local\Programs\Python\Python312\Lib\json\__init__.py", line 346, in loads
    return _default_decoder.decode(s)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\xxx\AppData\Local\Programs\Python\Python312\Lib\json\decoder.py", line 337, in decode
    obj, end = self.raw_decode(s, idx=_w(s, 0).end())
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\arlenzheng\AppData\Local\Programs\Python\Python312\Lib\json\decoder.py", line 355, in raw_decode
    raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 2 column 20 (char 21)
```

Relevant code:
[simulator/template.py/stop_simulation](https://github.com/confident-ai/deepeval/blob/v3.7.0/deepeval/simulator/template.py#L113-L117)
```python
Example JSON Output:
{{
    "is_complete": False,
    "reason": "The assistant explained how to forget password but ahas not confirmed that the user successfully set a new password."
}}
```
[models/llms/utils.py/trim_and_load_json](https://github.com/confident-ai/deepeval/blob/v3.7.0/deepeval/models/llms/utils.py#L7-L23)
```python
def trim_and_load_json(
    input_string: str,
) -> Dict:
    start = input_string.find("{")
    end = input_string.rfind("}") + 1
    if end == 0 and start != -1:
        input_string = input_string + "}"
        end = len(input_string)
    jsonStr = input_string[start:end] if start != -1 and end != 0 else ""
    jsonStr = re.sub(r",\s*([\]}])", r"\1", jsonStr)
    try:
        return json.loads(jsonStr)
    except json.JSONDecodeError:
        error_str = "Evaluation LLM outputted an invalid JSON. Please use a better evaluation model."
        raise ValueError(error_str)
    except Exception as e:
        raise Exception(f"An unexpected error occurred: {str(e)}")
```

### Steps to Reproduce

1. Use the conversation simulator with a model that mirrors the example JSON from the prompt, returning {"is_complete": False, ...}.
2. The simulator attempts to parse the model output via `trim_and_load_json`.
3. `json.loads` raises due to False being invalid in JSON.

### Expected Behavior

1. Prompt examples use valid JSON literals (true/false), so compliant models return parseable JSON.
2. trim_and_load_json successfully parses the model output.
3. conversation_simulator.simulate proceeds without error.

### Actual Behavior

1. Model returns `False` (influenced by example).
2. `json.loads` raises `JSONDecodeError`.
3. Simulation aborts with message: “Evaluation LLM outputted an invalid JSON. Please use a better evaluation model.”

### Impact

- Multiturn conversation generation fails even with otherwise well-behaved models.
- Error message is misleading; root cause is template guidance, not model capability.

### Root Cause

- Example JSON in `simulator/template.py` uses Python-style booleans (`False`) instead of JSON booleans (`false`), nudging LLMs to output invalid JSON.

### Proposed Fixes

- Check and update all prompt example JSON blocks to valid JSON: Replace True → true, False → false

### Additional Notes

- The current error message suggests “use a better evaluation model,” which can be misleading here. Consider amending it or adding context when the failure is likely due to non-JSON booleans.