A/B (x) Testing

A/B (x) Testing compares multiple model outputs and has experts, diverse users or both choose the most effective response.

How do you fine-tune an LLM with A/B (x) Testing?

A/B (x) Testing takes two or more sample answers from your model and asks specialists or a diverse crowd, and sometimes both, to select their favorite.

At Defined.ai, we use this method to make sure your model isn’t just technically accurate but also aligns with what users truly want. A/B (x) Testing is a great way to gather subjective feedback by asking people to compare different options and share their opinions.

How A/B (x) Testing Works

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Create

Generate two or more comparable model outputs
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Collect

Get real human feedback on which answers people prefer
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Align

Update your model to provide responses that resonate with your users
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Right on!

Your model stands out from the rest

LLM Fine-tuning Services

See all of our LLM Fine-tuning services

Fine-tuning Data Generation

Custom multimodal datasets for context-driven model tuning

Retrieval Augmented Generation

Add real-time knowledge for smarter, more accurate responses

RLHF & DPO

Human-guided alignment for preferred and relevant output

Red Teaming

Expert adversarial testing to identify risks and vulnerabilities

Model Stumping

Stress-test models with tough prompts to uncover failure points

LLM Benchmarks

Quantitative evaluation using standard and custom metrics

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