Emmimal P Alexander writes that while prompt engineering focuses on optimizing LLM inputs, managing these templates within evolving codebases often leads to production crashes when variables are renamed or removed. To solve this, she created `promptctl`, a Python tool that applies static analysis—similar to database schema migrations—to ensure prompt variable contracts match their call sites in the codebase.
- Performs PromptDiff (detects changes), Contract Validation (checks mismatches), and Impact Analysis (traces dependencies).
- Operates strictly via AST parsing, requiring zero LLM calls or API keys.
- Detects errors that unit tests often miss by mocking away the actual string formatting step.
- Performs PromptDiff (detects changes), Contract Validation (checks mismatches), and Impact Analysis (traces dependencies).
- Operates strictly via AST parsing, requiring zero LLM calls or API keys.
- Detects errors that unit tests often miss by mocking away the actual string formatting step.
>"Using DSPy to automatically create, evaluate, and optimize your prompts"
Manual prompt engineering is often slow and unreliable due to unpredictable inputs. DSPy addresses this by treating prompt development like traditional ML training. It automatically generates, evaluates (using "LLM-as-a-judge"), and optimizes prompts based on high-level task descriptions, providing a faster, more systematic way to build robust LLM applications.
Manual prompt engineering is often slow and unreliable due to unpredictable inputs. DSPy addresses this by treating prompt development like traditional ML training. It automatically generates, evaluates (using "LLM-as-a-judge"), and optimizes prompts based on high-level task descriptions, providing a faster, more systematic way to build robust LLM applications.
Guidelines for using large language models to improve Python code quality in casual usage.
