21:I[4606,["9139","static/chunks/9139-29aac3297803e837.js","5162","static/chunks/5162-971519dd5e179b15.js","1163","static/chunks/app/%5Blocale%5D/(with-footer)/ai/%5BwebsiteName%5D/page-93c09f9a3f0813c2.js"],""] 23:I[1895,["9139","static/chunks/9139-29aac3297803e837.js","5162","static/chunks/5162-971519dd5e179b15.js","1163","static/chunks/app/%5Blocale%5D/(with-footer)/ai/%5BwebsiteName%5D/page-93c09f9a3f0813c2.js"],""] 22:T4e6,

What is LLM Sandbox?

LLM Sandbox is an all-in-one environment to detect hallucinations, evaluate your prompts, select the right data to fine tune your models and track performance version after version. It focuses on open source data curation & management for Computer Vision, NLP and LLMs, helping users curate valuable unlabeled data to maximize domain coverage and model improvement.

How to use LLM Sandbox?

Register your metadata to Dioptra, diagnose model failure modes, curate valuable unlabeled data with active learning miners, and integrate with your labeling and retraining stack using Dioptra’s APIs.

LLM Sandbox's Core Features

Hallucination detection

Prompt evaluation

Data selection for fine-tuning

Performance tracking

Open Source data curation & management

Active learning miners

LLM Sandbox's Use Cases

#1 Improve model accuracy on hard cases

#2 Shorten training cycles

#3 Reduce labeling costs

LLM Sandbox

📋Introduction

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