@import url("https://use.typekit.net/zmy3zmk.css");

h1, h2, h3 {
  font-family: "inter-variable", sans-serif;
  font-variation-settings: "slnt" 0, "wght" 600;
}

Observability in Future AGI

Future AGI

Build, evaluate, observe, and optimize your LLMs applications 10x faster without Human-in-the-Loop or Ground Truth

What is Future AGI?

Overview

Quickstart

Concept

Learn how to set up and run experiments in Future AGI platform

How to Run Experiments

Admin & Settings

How to Build and Incrementally Improve RAG Applications in Langchain

How to evaluate RAG Applications

Documentation

Cookbooks

API Reference

SDK Reference

Examples

Sign Up

Hallucination

Multimodal AI

LLM as a Judge

Agent as a Judge

Evaluate Conversation

Evaluate Images

Ensure Safe and Inclusive AI

Identify Hallucination

Evaluate RAG Applications

Custom Evaluation Using Deterministic Eval

Validate Text Structures and Patterns

Validate JSON

Evaluate API Calls

Evaluate LLM Function Calling

Detect Prompt Injection

Evaluate Using LLM as a Judge

Evaluate Using Agent as a Judge

Ensure Data Compliance

Validate Emails

Ensure Valid Links

Evaluate Effective Use of Context Chunks

Detect Ambiguous Prompts Using Prompt Perplexity Eval

Evaluate Translation

Evaluate Summary

Verify If a Query Has Enough Context Using Context Sufficiency Eval

Conversation Coherence

Conversation Resolution

Deterministic Eval

Content Moderation

Context Adherence

Context Relevance

Completeness

Context Similarity

PII Detection

Toxicity

Tone

Sexist

Prompt Injection

Not Gibberish

Safe for Work Text Eval

Prompt/Instruction Adherence

Data Privacy Compliance

Is JSON

Regex

API Call

Custom Code

JSON Schema Validation

Context Sufficiency

Grading Criteria

Groundedness

Summarization Accuracy

Answer Similarity

Eval Output

Eval Context Retrieval

Eval Ranking

Eval Image Instruction

Score Eval

Summary Quality

Factual Accuracy

Translation Accuracy

Cultural Sensitivity

Bias Detection

LLM Function Calling

Length Evals

Contain

Prompt Perplexity

Chunk Attribution

Chunk Utilization

Valid Links

Is Email

Static Columns

Dynamic Columns

Change Column Type

Create Static Column

Understanding Observability

Components of Observability

Spans

Traces

Using Platform

Using Python SDK

Add Data Using SDK

Upload a File (JSON/CSV)

Create Synthetic Data

Manually Create a Dataset

Import from Hugging Face

Add from an Existing Dataset/Experiment

Introduction

Evaluation

Build

Experimentation

Observability

Optimization

Prompt Engineering

Protect

Dataset

Admin & Settings

Observability in Future AGI

Experiment

Observe