AI Engineering/Phase 14/Lesson 03

Reflexion: Verbal Reinforcement Learning

Gradient-based RL needs thousands of trials and a GPU cluster to fix a failure mode. Reflexion (Shinn et al., NeurIPS 2023) does it in natural language: after each failed trial, the agent writes a reflection, stores it in episodic memory, and conditions the next trial on that memory. This is the pattern behind Letta's sleep-time compute, Claude Code's CLAUDE.md learnings, and pro-workflow's learn-rule.

Build 1 hr Python (stdlib)

Prerequisites: Phase 14 · 01 (Agent Loop), Phase 14 · 02 (ReWOO)

#Learning Objectives

Name the three components of Reflexion (Actor, Evaluator, Self-Reflector) and the role of episodic memory.
Implement a stdlib Reflexion loop with binary evaluator, reflection buffer, and fresh re-attempts.
Choose between scalar, heuristic, and self-evaluated feedback sources for a given task.
Explain why verbal reinforcement catches errors that gradient-based RL would need thousands of trials to fix.

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.