LLM Fine-tuning Data & Services

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:

• Fine-tuning Data Generation
• Retrieval Augmented Generation
• Reinforcement Learning from Human Feedback & Direct Preference Optimization
• Red Teaming & Model Stumping
• LLM Benchmarks
• A/B (x) Testing

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.

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.

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

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.

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.

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

Reinforcement Learning with Human Feedback & Direct Preference

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.

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 data marketplace collection 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.

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

Red Teaming tries to force an LLM 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

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

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 the 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.

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.