Automatically Optimize AI Prompts with OpenAI Using OPRO & DSPy Methodology

Description

This workflow implements cutting-edge concepts from Google DeepMind's OPRO (Optimization by PROmpting) and Stanford's DSPy to automatically refine AI prompts. It iteratively generates, evaluates, and optimizes responses against a ground truth, allowing you to "compile" your prompts for maximum accuracy. Why this is powerful Instead of manually tweaking prompts (trial and error), this workflow treats prompt engineering as an optimization problem: OPRO-style Optimization**: The "Optimizer" LLM analyzes past performance scores and reasons to mathematically deduce a better prompt. DSPy-style Logic**: It separates the "Logic" (Workflow) from the "Parameters" (Prompts), allowing the system to self-correct until it matches the Ground Truth. How it works Define**: Set your initial prompt and a test case with the expected answer (Ground Truth). Generate**: The workflow generates a response using the current prompt. Evaluate**: An AI Evaluator scores the response (0-100) based on accuracy and format. Optimize**: If the score is low, the Optimizer AI analyzes the failure and rewrites the prompt. Loop**: The process repeats until the score reaches 95/100 or the loop limit is hit. Setup steps Configure OpenAI: Ensure you have an OpenAI credential set up in the OpenAI Chat Model node. Customize: Open the Define Initial Prompt & Test Data node and set your initial_prompt, test_input, and ground_truth. Run: Execute the workflow and check the Manage Loop & State node output for the optimized prompt. An n8n automation workflow template by Shun Nakayama.

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Categories

• Automation
• AI

Tags

• n8n
• workflow
• code
• ai-agent
• openai-chat-model
• structured-output-parser

Capabilities

• 4 nodes
• 1 views

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