Evaluations

Effortlessly audit thousands of prompts using Adaline’s AI-powered evaluation suite—run custom checks, visualize pass/fail results, and optimize every iteration.

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Scale Prompt Evaluations with AI

LLM Evaluation

Continuous Performance Analysis

Instantly detect, categorize and trace failures back to their root causes—so you can fix critical issues before they impact users.

Generated Evaluation

Auto-Generated Validation Code

Describe your desired checks in plain English and let Adaline generate the corresponding JS validation script automatically.

Full Suite of Evaluation

Built-In Evaluation Templates

Get up and running instantly with ready-made tests for common tasks (context recall, rubric scoring, schema validation, pattern recognition, completion length) that you can customize.

Roll Back

One-Click Version Revert

Browse past evaluation runs by date, score and outcome—then pick any snapshot to revert in seconds, complete with audit logs.

Evaluations

Scale Prompt Evaluations with AI

Continuous Performance Analysis

Auto-Generated Validation Code

Built-In Evaluation Templates

One-Click Version Revert

FAQs

What is LLM evaluation and why is it important?

What are the key metrics used to evaluate LLMs?

What is the difference between model evaluation and system evaluation in LLMs?

What are common benchmarks used for LLM evaluation?

How do you detect and measure hallucinations in LLM outputs?

What role does user feedback play in evaluating prompt quality?

How can prompt evaluations improve the accuracy of LLM responses?

What are best practices for conducting LLM and prompt evaluations?

How do you evaluate prompts for edge cases and ambiguous queries?

What does “LLM as a Judge” mean in AI evaluation workflows?