aimodelmatch/frontend-webapp

AI Model Match

AI Model Match is an open-source platform that helps product teams release, test, and optimize prompt configurations for AI-powered applications. It replaces the traditional, manual trial-and-error approach with an automated system that continuously identifies the best-performing prompts for each use case, so teams can build, measure, and improve AI experiences with speed and confidence.

By organizing AI experimentation into use cases, steps, and flows, AI Model Match enables teams to:

• Rapidly test and compare different prompt configurations
• Collect feedback from integrated systems
• Continuously improve AI behavior without disrupting the user experience

🚀 Overview

AI Model Match enables teams to:

• Define use cases as product goals, such as providing recommendations, generating content, or planning a trip.
• Create flows, representing multiple candidate strategies to achieve each goal.
• Organize flows into steps, precise configurations that guide AI behavior at each stage of the interaction.
• Intelligently distribute traffic across flows to maintain consistency while optimizing performance.
• Collect feedback as combination of different aspects to improve AI performance.

This system empowers Product teams to iterate independently, accelerate release cycles, and minimize risk, while end users benefit from AI interactions that steadily improve.

📐 Core Concepts

1. Use Case

   • Represents a specific product goal or objective.
   • Defines the scope of experimentation and the metrics for success.

2. Flow

   • A candidate configuration of prompts to achieve a use case.
   • Multiple flows can be defined to explore different approaches.

3. Step

   • Each flow is composed of steps, with each step defining a precise prompt configuration.
   • Steps allow fine-grained control of AI behavior at each stage of interaction.

4. Session & Correlation ID

   • Each request is tied to a unique correlation ID.
   • Once a flow is selected for a correlation ID, all subsequent steps in that session use the same flow, ensuring predictable and coherent experiences.

5. Feedback

   • Ratings (1–5) and optional notes can be submitted for each session.
   • Feedback is aggregated per flow to guide automated flow selection and optimization.

⚙️ Rollout Strategy

AI Model Match automates the rollout of flows using a controlled, multi-phase approach:

1. Warmup

   • New flows are gradually introduced until they reach a target traffic percentage.

2. Adaptive

   • Traffic is automatically shifted toward higher-performing flows based on feedback.
   • Flows that receive positive feedback gain more traffic until one flow converges to 100%.

3. Escape

   • Configurable rollback conditions trigger automatic reversion if a flow un***forms (e.g., ≥10 evaluations with an average score < 2/5).
   • Protects user experience while minimizing risks.

💡 Benefits

For Product Managers

• Accelerates iteration cycles without heavy engineering dependency.
• Provides a data-driven approach to evaluating AI strategies.
• Enables safe experimentation with automated traffic distribution and rollback.

For End Users

• Consistent, high-quality AI experiences.
• Interactions improve over time based on feedback.

Business Value

• Reduces time and cost of AI experimentation.
• Identifies the best-performing strategies quickly.
• Lowers risk while scaling successful configurations.

🎯 Target Audience

• Product Managers looking to test their AI products evolutions quickly and independently.
• Development Teams integrating AI-driven workflows into their applications.
• End Users who benefit from AI interactions that are consistent, coherent, and continuously improving.

📈 How It Works

1. Define a use case representing a product goal.
2. Create one or more flows with structured steps as candidate AI behaviour.
3. Release flows and let AI Model Match manage traffic distribution and feedback collection.
4. Monitor performance as the system automatically optimizes flow selection based on collected feedback.

🛠️ Technical Details

• AI Model Match is implemented as an open-source microservice.
• Provides APIs to external systems for runtime prompt configuration.
• Supports fine-grained control over AI interactions through use cases, flows, and steps.
• Correlation IDs ensure consistent execution across multi-step flows.
• Feedback collection APIs enable automated performance evaluation and optimization.
• Can be deployed standalone or integrated with existing production environments.
• Future plans may include a SaaS version to abstract deployment and infrastructure management.

🔗 Contributing

AI Model Match is open-source and welcomes contributions from the community.

• To report bugs or request features, open an https://github.com/ai-model-match/backend/issues.
• To contribute code or documentation, submit a https://github.com/ai-model-match/backend/pulls.
• Feedback and suggestions are always appreciated!