LLM Fine-tuning Data & Services

The best AI models need relevant, high-quality ethical data and expert human evaluation—all at scale. Defined.ai brings global AI specialists together to support your LLM fine-tuning, whether you're working with open-source models or your own proprietary foundation model.
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Defined.ai offers end-to-end services for fine-tuning AI models, including Fine-tuning Data Generation, Retrieval Augmented Generation and Reinforcement Learning from Human Feedback & Direct Preference Optimization. We enhance model safety and robustness through Red Teaming & Model Stumping, validate improvements with LLM Benchmarks and optimize performance via A/B (x) Testing, empowering enterprises to build better AI systems.

Want to get to the top of the LLM Leaderboard?

Ensuring your AI models are functional, accurate, reliable and high-performing requires supervised fine-tuning that only human expertise can bring. Whatever the application, Defined.ai provides the specialized machine learning datasets and LLM testing, evaluating and benchmarking you need for cutting-edge AI optimization.

Open-source generative AI models have revolutionized the artificial intelligence landscape. OpenAI’s GPT, Meta’s LLaMa, Google Gemini and DeepSeek-R1 are all powerful, but by democratizing access to AI foundation models, customizing your own is the new challenge. To make your AI application stand out and targeted to your use case, Defined.ai offers comprehensive LLM training and evaluation frameworks.

Don't let your AI foundational model hold you back. Using biased, poor-quality or copyrighted data carries potentially expensive operational, legal, security and privacy risks. Fine-tuning your LLM with high-quality, responsibly-sourced AI training data is critical to protect your reputation and bottom line. Unlike other AI training data companies, Defined.ai’s core AI ethics ensure our contributors receive fair compensation, and the data they generate is secure, fully consented for AI model training and copyright-cleared.

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Explore our AI Marketplace

Defined.ai has the world’s largest AI marketplace so you can find the exact data you need. Accurate, scalable datasets—quality checked, AI-ready and ethically sourced—covering over 70 languages in more than 120 markets. Explore our AI marketplace

We are thankful for Defined.ai’s unrelenting efforts creating video, audio and word datasets. Carefully scripted and crafted yet delivered at an extremely high velocity, they allow our neural networks to iterate and improve continually. We are delighted by their rigor and reliability. When all levers are churning and engines are firing—music is created.

Saurabh Sasxena, Head of Technology at Uniphore

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Speech & Audio

920K+ hours of monologues, dialogues and real conversations
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Images & Videos

11M+ photos, illustrations and graphs & 100K+ hours of cellphone, CCTV camera and professional footage
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Music & Sound Effects

3M+ vocal, instrumental and audio tracks
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Text, Words & Code

50B+ tokens
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New to AI Marketplaces?

Read our blog for tips on:

  • Skipping the data grind with easy access to high-quality, ready-to-use AI training data
  • Building and launching AI projects faster—without needing a full in-house data team
  • Protecting your business with ethically-sourced, privacy-compliant data that keeps your models responsible and legal

Fine-tuning Data Generation

What is fine-tuning data generation?

Fine-tuning data generation gives your AI model more natural, diverse and precise responses, resulting in greater contextual accuracy. At Defined.ai, we help fine-tune your LLM to deliver the right output for your users every time. According to your specifications, we deliver bespoke annotated datasets like:

  • Multi-modal content with text, audio and video
  • Complex interactions
  • Simple question-answer pairs
  • Multi-turn reasoning tasks

With fine-tuning data generation, you can easily create custom model variants tailored to different audiences or demographics. You can even train your model to ask follow-up questions for more careful, context-driven interactions.

Customize your AI project

Our proprietary data generation workflow platform is flexible and multi-modal first. User-friendly and with a built-in quality control mechanism, it’s fully customizable to your AI project. Check out some fine-tuning case studies

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How it works

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Define

Identify a specific task you want your model to perform like giving set answers to specific groups or asking follow up questions
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Source

Get custom datasets designed, produced and labelled by our experts and global crowd
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Refine

Improve your model’s precision and naturalness
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Success!

Your model is now fine-tuned

Visit our AI marketplace for AI-ready datasets

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STEM Books and Articles

Retrieval Augmented Generation

What is RAG in AI?

Retrieval Augmented Generation (RAG) improves your LLM’s accuracy and makes its answers more reliable. By giving your AI model access to specific information through a database or other sources (called “grounding documents”), you can boost its responses so they’re always up to date and relevant to your use case, customer base or brand voice.

At Defined.ai, we’ve seen how powerful RAG AI can be when accuracy is a must but the model doesn’t need to know everything. Focusing the scope of the data ensures answers are highly relevant and correct, making it perfect for specialized topics. It’s also a more affordable option if you only need to add or update specific information for your model rather than retraining it completely. Speak to an expert

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Setting up training data for RAG, testing the outcome and adding annotations all take time. Let our specialists generate questions and answers from your grounding documents, provide cited answers and more so you can focus on your business!

How it works

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Supply

Provide the information source or guidance documentation you want your model to draw on
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Generate

Get questions and answers created by our specialists based on your bespoke material
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Enhance

Improve your model’s subject-specific accuracy
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Congrats!

Your model has been upgraded

Reinforcement Learning with Human Feedback & Direct Preference Optimization

RLHF vs DPO

You can greatly improve your model’s accuracy, reliability, alignment with human expectations and user trust through Reinforcement Learning with Human Feedback (RLHF) and Direct Preference Optimization (DPO). So, what’s the difference?

RLHF ensures your LLM data is correct and complete (and, if not, why), for more accurate predictions, less bias and more efficient use of training resources. The most important letter here is H: the human expertise used to evaluate your model’s output that will take it to the next level.

DPO helps adjust your LLM’s tone ensuring it’s just right—whether you need more formality or a more casual feel, longer or shorter answers. Because your users and customers will all have their own response preferences, this process helps you connect with your audience by speaking their language.

To ensure your model meets user expectations and delivers accurate, relevant results, feedback from the right people is key. Whether domain experts for accuracy or a diverse group of contributors to avoid bias, at Defined.ai we connect you with the right crowd for the job.

How it works

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Detect

Identify where your model’s output is inaccurate or biased, or choose the response tone you want it to give
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Review

Get feedback from our specialists and a global crowd
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Optimize

Improve your model’s accuracy and relevance and remove bias
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Great job!

Your model's performance has been boosted

Healthcare datasets for machine learning

Healthcare is an industry that relies on accurate, up-to-date information to make the best decisions for patients. Check out our collections of over 20,000 Health and Physiotherapy articles, 42,000 MRI scan images and 100,000 dental x-rays for your healthcare business AI solutions. Explore the full data marketplace

Red Teaming & Model Stumping

Red Teaming & Model Stumping help you find, evaluate and fix your AI model’s vulnerabilities and weaknesses.

Red Teaming: Keep your LLM on side

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Red Teaming tries to force an LLM to do things that it shouldn’t, like providing illegal or dangerous information. Our expert AI Red Team will stage an attack on your AI model (known as a “prompt injection”) to help you avoid litigation and keep your users safe.

Model Stumping: Logical AI model training

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AI models are complex, but sometimes simple questions can throw them off and you can't explain to them why. Through Model Stumping our specialists will spot the weaknesses in your LLM’s logic and correct it.

How it works

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Spot

Identify when your model could provide unsuitable or illogical information
Evaluate your model’s weaknesses through our experts’ prompts

Assess

Evaluate your model’s weaknesses through our experts’ prompts
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Secure

Update your model for safer, more logical answers
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Excellent!

Your model has advanced
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Read our Legal Director’s thoughts on AI governance and compliance

Director of Legal at Defined.ai Melissa Carvalho shares her perspectives on how AI companies can implement more effective ethical practices to avoid making headlines from the misuse of data. She shares insights on the evolving legal landscape around AI and copyright, highlighting why transparency in data sourcing is so important.

LLM Benchmarks

LLM benchmarking uses controlled metrics and datasets to compare your model’s abilities with industry standards to see what’s working and where you need help. For many projects, what seems to be the limit of AI is simply inadequate LLM benchmarks.

Our LLM evaluation framework ensures your AI model is rigorously tested with more than just standard benchmarks. There are plenty of benchmarks out there, but many have issues. They can be inaccurate, biased or only cover a narrow part of a domain, making them less effective for general use.

Our experts can refine and enhance your current evaluation process and continuously maintain and develop it across the AI development lifecycle.

How it works

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Report

Submit your current AI model benchmarks
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Collaborate

Work with our experts to develop your benchmarks in line with industry standards or tailored to your business
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Advance

Continuously improve your model’s output through ongoing evaluation
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Amazing!

Your model is at the next level
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Find out what LLMs can do for your business

The world of artificial intelligence (AI) is evolving at breakneck speed, and the rise of large language models is one of the game-changing developments in this field. But what exactly are these models, and why should you as a business owner or executive care? If you’re looking to stay ahead in the rapidly shifting business landscape, you definitely want to take advantage of this.

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 it 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
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Learn how Defined.ai makes fine-tuning your LLM ethical

“Product leaders still focus too much on technology and not enough on business value and trust”. In the Emerging Tech Impact Radar: Intelligent Simulation report, Gartner® predicts GenAI models using simulation data “will underpin 20% of strategic business decisions by 2030, up from approximately 1% in 2024”. So, if we don’t focus on the technology, what’s left?


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