Split Test AI Prompts Using Supabase & Langchain Agent
This article describes a workflow that allows A/B testing of different prompts for an AI chatbot powered by Langchain and OpenAI, using Supabase to persist session state and randomly assign users to baseline or alternative prompts.
Why it matters
Prompt optimization is a critical aspect of building high-performing AI chatbots, and this workflow provides a reliable way to test and compare different prompt versions.
Key Points
- 1Randomized A/B split testing of AI prompts per session
- 2Supabase database used for session persistence
- 3Langchain Agent and OpenAI GPT-4 integration
- 4PostgreSQL used for maintaining chat context
Details
The workflow described in this article enables prompt optimization for AI chatbots by running controlled experiments on different prompt versions. It uses Supabase to store session information and randomly assign users to either a baseline or alternative prompt. The selected prompt is then passed into a Langchain Agent using the OpenAI Chat Model, with PostgreSQL used as the chat memory for multi-turn conversation support. This allows for reliable comparison of prompt performance over time, which is crucial for maximizing the effectiveness of AI assistants.
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