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Introduction
  • What is Future AGI?
Evaluation
  • Overview
  • Quickstart
  • Concept
    • Overview
    • Guardrail
    • Hallucination
    • Multimodal AI
    • Agent as a Judge
  • How To
  • Eval Definition
Knowledge Base
  • Overview
  • Concept
  • How To
Dataset
  • Overview
  • Concept
  • Adding Dataset
  • Create Dynamic Column
  • Add Annotations
  • Change Column Type
  • Create Static Column
  • Create Synthetic Data
  • Experimentation
Prototype
  • Overview
  • Quickstart
  • Evals for Prototype
  • Choose Winner
Observe
  • Overview
  • Quickstart
  • How to run evals?
  • Sessions
  • Alerts and Monitors
Tracing
  • Overview
  • Concept
  • Instrumentation ( Auto )
  • Manual Tracing
Optimization
  • Overview
  • Concept
  • How To
Prompt Workbench
  • Overview
  • Concept
  • How To
Protect
  • Overview
  • Concept
  • How to Use Future AGI Protect
MCP
  • MCP Server
Admin & Settings
  • Administration Panel
FAQs
  • Frequently Asked Questions (FAQ)
  • Community
  • Sign Up
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  • Community
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Concept
Overview
Documentation
Cookbooks
Release Notes
SDK Reference
Documentation
Cookbooks
Release Notes
SDK Reference
Concept

Overview

This section provides the foundational concepts behind AI evaluation practices. Understanding these core principles is essential for building reliable, safe, and effective AI applications that meet performance standards and compliance requirements.

This section covers:

  • Core evaluation paradigms for assessing AI-generated outputs
  • Key metrics and methodologies for quantifying model performance
  • Best practices for implementing systematic evaluation procedures x

Guardrails

Learn about implementing safety and compliance safeguards for AI systems

Hallucination

Understand how to detect and mitigate AI fabrications and inaccuracies

Multimodal AI

Explore evaluation strategies for AI systems that process multiple data types

Agent Judge

Learn about using AI to evaluate other AI systems’ outputs

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