Grab your coffee. Here are this week’s highlights.
๐ Today’s Picks
MLflow: Built-in Scorers for LLM Evaluation Without Custom Logic
Problem
Ensuring consistent LLM quality means checking correctness, relevance, and guideline adherence.
But writing custom evaluation logic for each criterion is tedious.
Solution
MLflow provides pre-built scorers for common evaluation patterns with simple decorator syntax for custom metrics.
Key capabilities:
- Built-in scorers for correctness and guideline compliance
- Simple @mlflow.scorer decorator for custom metrics
- Standardized evaluation patterns across projects
- Visual summary of all assessment results in MLflow UI
๐ 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
๐ข ANNOUNCEMENTS
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Founding pricing ends March 31, 2026.
โ๏ธ 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
Looking for a specific tool? Explore 70+ Python tools โ
๐ 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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