⚒️ Automatically optimize AI prompts with OpenAI using OPRO & DSPy methodology

⚡ 388 views · ⚒️ Engineering

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

1. Configure OpenAI: Ensure you have an OpenAI credential set up in the OpenAI Chat Model node.
2. Customize: Open the Define Initial Prompt & Test Data node and set your initial_prompt, test_input, and ground_truth.
3. Run: Execute the workflow and check the Manage Loop & State node output for the optimized prompt.

🔗 Nodes Used

AI Agent, OpenAI Chat Model, Structured Output Parser

📥 Import

Download workflow.json and import into n8n: Workflow menu → Import from File

📖 Importing guide · 🔑 Credential setup