DIY #19 - Evaluator-Optimiser LLM Agent with LangChain

2025-01-29 · Source: Machine Learning Pills · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, long

Summary

The "Evaluator-Optimiser" pattern for Large Language Model (LLM) agents enhances output quality by implementing a feedback loop between a Generator LLM and an Evaluator LLM. Unlike one-shot LLM interactions, this pattern involves an iterative process where the Generator produces an initial response, which the Evaluator then critically assesses against predefined criteria. If the output fails, the Evaluator provides structured feedback to the Generator, prompting a revision. This cycle repeats until the output meets the quality standards or a maximum number of attempts is reached. The article demonstrates this pattern using LangChain and Pydantic to build a self-healing Python code generator that refines code for correctness, efficiency, and style, specifically tackling an O(n) anagram checker with case-insensitivity.

Key takeaway

For AI Engineers building LLM agents for high-stakes tasks like code generation or legal drafting, adopting the Evaluator-Optimiser pattern is crucial. This approach ensures outputs meet stringent quality, efficiency, and style requirements by enabling self-correction through iterative feedback. Implement a `max_attempts` counter to prevent infinite loops and carefully balance the Evaluator's strictness to provide actionable feedback, leading to more robust and reliable AI systems.

Key insights

The Evaluator-Optimiser pattern uses iterative feedback between two LLMs to achieve higher quality outputs for complex tasks.

Principles

• Separate creative generation from critical evaluation.
• Iterate on outputs to maximize quality.
• Enforce specific business rules via evaluation.

Method

A Generator LLM produces a response, an Evaluator LLM analyzes it against criteria, and if it fails, feedback is looped back to the Generator for revision until criteria are met or max attempts are reached.

In practice

• Use `temperature=0.7` for Generator LLM creativity.
• Use `temperature=0.0` for Evaluator LLM strictness.
• Define evaluation criteria with Pydantic for structured feedback.

Topics

• LLM Agents
• Evaluator-Optimiser Pattern
• LangChain
• Code Generation
• Feedback Loops

Best for: AI Engineer, Machine Learning Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning Pills.