Product

Steering the AI application with comprehensive AI eval capabilities

Objectives

Steering the AI Application

Guide and control AI behavior through systematic evaluation

Reference-Free & Reference-Based Evals

Run both evaluation types for comprehensive AI quality assessment

4 Stages to Evaluation Readiness

A systematic approach to building robust AI evaluation capabilities

1

Initiation

Proactive: Scenario Based

Define evaluation scenarios upfront based on expected use cases and edge cases

Reactive: Production Traces

Capture and analyze real-world production interactions to identify evaluation needs

2

Generation

Generate Silver Datasets

From Grounded Knowledge Base

Leverage your existing knowledge base to generate evaluation datasets with verified ground truth

From External Sources

Coming soon: Integrate external data sources for broader evaluation coverage

3

Refinement

Identify Best Candidates

Filter and select the highest quality evaluation samples

Open & Axial Coding

Apply qualitative research methods to categorize and structure evaluation data

AI Assisted Gap Analysis

Coming soon: Automated visualization and gap identification

4

Codification

Subject Matter Expert Review

Domain experts validate and approve evaluation criteria and datasets

Finalize Golden Dataset

Create the authoritative dataset for reference-based assessments

Create Rubrics

Define scoring criteria for reference-free assessments