Creating Custom AI Evaluators in Stax for Specific Product Criteria

Managing and Creating Evaluators in Stax

• 💡 Stax offers an evaluator gallery for managing and creating custom evaluators beyond common GenAI criteria.
• 🎯 The platform allows developers to define specific criteria important for their AI products, moving beyond generic metrics.

Automating Evaluations with LLM-Based Autoraters

• 🚀 The video introduces the concept of LLM as a judge or autoraters to automate the evaluation process.
• 🧠 This is demonstrated with an AI travel agent use case, where custom evaluators can identify "hidden gems" recommendations.
• ⏱️ Manual testing for such nuanced criteria is time-consuming and does not scale, making automated evaluators a superior solution.

Defining Custom Evaluator Prompts

• 🛠️ When creating a new evaluator, users define the base LLM to act as the judge model.
• ✍️ The core of a powerful evaluator is the prompt, which instructs the judge LLM on how to score outputs.
• 🔍 To achieve the best results, prompts should be highly specific, detailing criteria like what constitutes a "hidden gem" (e.g., non-obvious, authentic, specific location).
• ✅ Including examples within the prompt further aids the LLM in understanding the desired criteria.

Mapping Rating Categories and Metrics

• 📊 Evaluators require clear rating categories (e.g., "hidden gem," "popular favorite," "tourist trap") to grade outputs.
• 📈 These categories are then mapped to metric scores and colors, enabling visualization and aggregation of results in project analytics.

Aligning Evaluators with Quality Standards

• 🔬 It's recommended to compare auto-reader scores against human ratings on a sample set of outputs.
• 🔧 If ratings are not aligned, the evaluator prompt should be iteratively tweaked until it matches the desired quality bar, ensuring scalable and confident evaluations.