YAML Inputs to LLMs
The tradeoffs between natural language and structured interfaces for LLMs. While natural language allows flexible, accessible interaction, it creates challenges for software engineering due to non-deterministic outputs. They propose using YAML as an alternative interface, arguing it provides better structure and testability while maintaining human readability. This approach would enable proper software engineering practices like validation, linting, and unit testing when building LLM-powered automation tools.
Show Notes
Natural Language vs Deterministic Interfaces for LLMs
Key Points
Natural language interfaces for LLMs are powerful but can be problematic for software engineering and automation
Benefits of natural language:
- Flexible input handling
- Accessible to non-technical users
- Works well for casual text manipulation tasks
Challenges with natural language:
- Lacks deterministic behavior needed for automation
- Difficult to express complex logic
- Results can vary with slight prompt changes
- Not ideal for command-line tools or batch processing
Proposed Solution: YAML-Based Interface
- YAML offers advantages as an LLM interface:
- Structured key-value format
- Human-readable like Python dictionaries
- Can be linted and validated
- Enables unit testing and fuzz testing
- Used widely in build systems (e.g., Amazon CodeBuild)
Implementation Suggestions
- Create directories of YAML-formatted prompts
- Build prompt templates with defined sections
- Run validation and tests for deterministic behavior
- Consider using with local LLMs (Ollama, Rust Candle, etc.)
- Apply software engineering best practices
Conclusion
Moving from natural language to YAML-structured prompts could improve determinism and reliability when using LLMs for automation and software engineering tasks.
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