How to optimize Wenxin Yiyan prompts for generating production-ready Python code

Wenxin Yiyan (ERNIE Bot) handles Python differently than Claude or GPT-4; it tends to be overly verbose with comments and occasionally drifts into "textbook" style code that isn't actually production-ready. After spending a month forcing it to write my backend logic, I've found that the key to getting clean, maintainable code is strictly constraining its "creative" side through systemic role-definition and structural requirements.

The biggest mistake is asking it to "write a function to do X." That usually results in a script with no error handling and generic variable names. Instead, I now use a "Technical Specification" wrapper. I tell it to act as a Senior Backend Engineer who prioritizes PEP 8 and type hinting.

Here is the prompt structure that actually works for me:

# Role: Senior Python Engineer (Production Focus)
# Constraint: No conversational filler. Output only the code and a brief technical explanation.
# Standard: 
- Use Type Hinting for all function signatures.
- Implement robust error handling using try-except blocks with specific exceptions.
- Follow PEP 8 strictly.
- Include a basic pytest suite for the logic.

# Task: [Insert detailed requirement here]
# Context: [Insert existing codebase context or library versions]

One specific "gotcha" with Wenxin Yiyan is its tendency to use outdated library versions or hallucinate parameters for niche APIs. To fix this, I stop letting it guess. I now paste the relevant snippet of the official documentation into the prompt. If I'm using a specific version of FastAPI or Pydantic, I explicitly state: Use Pydantic v2 syntax (e.g., model_validate instead of from_orm).

For productivity gains, I've shifted from asking for the whole file to asking for "modular components." If you ask for a whole module, the AI often cuts corners toward the end to save tokens. I break it down into:
Data Models -> Service Logic -> API Endpoints -> Tests.

When the generated code is too "chatty" or includes unnecessary print statements, I use a refinement prompt to strip the noise:

Refactor the above code: remove all print() statements, replace them with the logging module, and ensure all docstrings follow the Google Style Guide.

Another pro tip for those integrating this into a workflow: Wenxin Yiyan is surprisingly good at generating requirements.txt or pyproject.toml if you give it the final code. I usually run the code through a linter first, then feed any errors back into the bot. It’s much faster to say Fix this KeyError in line 42 than to ask it to "debug the code."

My current optimized workflow for a new feature:
Define the interface (Input/Output types) -> Generate the logic core (with the spec wrapper) -> Request edge-case tests -> Manual review for "AI-isms" (like redundant comments).

The result is a shift from spending 30 minutes cleaning up "AI code" to spending 5 minutes verifying a nearly finished module.