MLflow: Built-in Scorers for LLM Evaluation Without Custom Logic
Plus version prompts like Git commits
Grab your coffee. Here are this week’s highlights.
📅 Today’s Picks
MLflow: Built-in Scorers for LLM Evaluation Without Custom Logic
Problem
Evaluating LLM outputs for correctness, relevance, and guideline adherence requires writing custom evaluation logic for each criterion.
This creates repetitive boilerplate code and slows down your iteration cycle.
Solution
MLflow provides pre-built scorers for common evaluation patterns like correctness and guideline adherence.
Key benefits:
Pre-built scorers for correctness, relevance, and safety checks
Simple decorator syntax for custom metrics
Automatic tracking of scores, rationales, and metadata in MLflow
Combine multiple scorers in a single evaluation run
Integrate with existing MLflow workflows without additional setup.
🧪 Run code | ⭐ View GitHub
🔄 Worth Revisiting
Swap AI Prompts Instantly with MLflow Prompt Registry
Problem
Finding the right prompt often takes experimentation: tweaking wording, adjusting tone, testing different instructions.
But with prompts hardcoded in your codebase, each test requires a code change and redeployment.
Solution
MLflow Prompt Registry solves this with aliases. Your code references an alias like “production” instead of a version number, so you can swap versions without changing it.
Here’s how it works:
Every prompt edit creates a new immutable version with a commit message
Register prompts once, then assign aliases to specific versions
Deploy to different environments by creating aliases like “staging” and “production”
Track full version history with metadata and tags for each prompt
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☕️ Weekly Finds
zipline [Finance] - Pythonic algorithmic trading library with event-driven backtesting for building and testing trading strategies
outlines [LLM] - Structured text generation library that constrains LLM outputs to follow specific schemas, formats, and data types
responses [Testing] - Utility library for mocking out the Python Requests library in tests with simple decorators and context managers
📚 Latest Deep Dives
5 Python Tools for Structured LLM Outputs: A Practical Comparison - Compare 5 Python tools for structured LLM outputs. Learn when to use Instructor, PydanticAI, LangChain, Outlines, or Guidance for JSON extraction.
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