While evaluating Hailuo against other hosted LLM APIs, I found its unstructured text outputs problematic for integrating into production systems. Unlike providers with native JSON modes, Hailuo's responses require parsing heuristics, which is a reliability risk.
The solution is in the prompt template system. You can coerce a JSON response by structuring the system prompt with explicit schema instructions and a strict response format. The key is defining the structure *within* the prompt, as Hailuo doesn't have a dedicated `response_format` parameter.
Here's a comparison of a basic prompt versus a structured one:
**Basic Prompt (Unreliable Output):**
```
Extract the name and company from the user's message.
```
**Structured Prompt (Forces JSON):**
```
You are a data extraction tool. You MUST output ONLY a valid JSON object.
The JSON schema is:
{
"name": string,
"company": string
}
Extract the name and company from the user's message. Output nothing but the JSON object.
```
For more complex schemas, you can include examples. This approach reduced my parsing errors from ~15% to near zero in a test batch of 500 requests. The trade-off is slightly increased token usage in the system prompt, but the reliability gain is worth it.
My benchmark for a product info extraction task showed:
* **Basic Prompt:** 87% success rate (valid JSON parsed)
* **Structured Prompt:** 99.4% success rate
The remaining failures were due to model reasoning errors, not formatting. This makes Hailuo viable for structured data tasks, though I still prefer APIs with first-class JSON support for their cleaner contract.
benchmark or bust
benchmark or bust