Supervised Fine Tuning
Supervised fine tuning (SFT) is essential for adapting large language models (LLMs) to deliver high-precision performance on domain-specific tasks. As organisations operationalise AI, SFT enables greater control, alignment with business objectives, and measurable improvements in model outcomes.
What Is Supervised Fine Tuning?
Supervised fine tuning (SFT) enables organisations to adapt pre-trained AI models to their specific needs with high-quality, LLM training data. This targeted approach transforms general-purpose models into domain-optimised solutions that deliver greater accuracy, efficiency, and business value.
Pre-Training vs Fine Tuning: Understanding the Difference
Large language models are first pre-trained on general AI data to build a foundational understanding of language, context, and structure. Models are then refined with supervised fine tuning to optimise performance in specific tasks and domains, such as science or economics. Curated SFT data enables models to perform with greater accuracy in nuanced real-world applications.
- Large, multi-domain datasets
- Filtered for accuracy
- Smaller, specialised datasets
- Curated for specific use cases
What is an SFT Dataset?
SFT data consists of structured datasets curated to train models for a specific task or domain. In the context of supervised learning, these datasets include both input data (e.g., text, images) and the corresponding output (e.g., categories, responses) that guide the model during training. A high standard of AI data quality is essential to ensuring the model learns the right patterns to optimise task-specific outputs.
How It Works: Supervised Fine Tuning Step-by-Step
Supervised fine tuning (SFT) is a meticulous process, requiring several stages to enhance model performance.
Data Collection and Preparation
The first stage of the fine-tuning process involves domain-specific AI data collection to build datasets that reflect the tasks the model is intended to perform. This data should be high-quality, diverse, and relevant to the intended use case. In machine learning pipelines, data preparation typically includes cleaning, normalising, and transforming raw input data into a usable format for model training.
Data Annotation and Quality Assurance
SFT depends on high quality data for success. Once data is collected, it must be annotated and evaluated to create accurate, consistent, and representative datasets. Data annotation strategies vary based on the desired outcome and include tasks like tagging sentiment, categorising entities, and identifying linguistic relationships. Appen specialises in creating complex SFT datasets to enhance model performance in nuanced and challenging use cases like summarisation and chain-of-thought reasoning.
Fine-Tuning Model Weights
Fine-tuning leverages structured data to adjust the pre-trained model’s weights and minimise errors on specific tasks. This typically involves training the model on the SFT dataset with a lower learning rate to ensure that it retains its generalised knowledge while specialising in the new task. Techniques such as gradient descent and backpropagation are commonly used in this phase to optimise model performance.
Evaluation and Iteration
After fine-tuning, the model undergoes rigorous evaluation using predefined performance metrics. Model evaluation benchmarks vary based on the intended use case but typically include accuracy, F1 score, and domain-specific KPIs. Based on the evaluation results, the model may require further fine tuning – such as adjusting hyperparameters, increasing the dataset size, or re-annotating data—to improve results. This iterative cycle ensures continuous model improvement and efficiency.
SFT Techniques
How Appen Supports Supervised Fine Tuning
Appen provides end-to-end support for organisations fine-tuning AI models—helping you unlock domain-specific performance with scalable, high-quality data solutions.
Curated SFT Datasets
We source and prepare high-quality, domain-relevant data tailored to your specific use case. From finance and healthcare to retail and customer support, our curated datasets provide the foundation for effective supervised fine tuning.
Human Annotation at Scale
Appen delivers accurate, high-volume annotations powered by a global crowd and expert linguistic teams. Our QA workflows ensure every annotation meets the standards needed to fine-tune large language models with precision.
Model Evaluation & Iterative Fine-Tuning
Our team supports continuous evaluation with human-in-the-loop feedback, enabling rapid iteration and refinement. We help you measure what matters—accuracy, relevance, safety—and improve your model with each cycle.
Appen in Action
Appen supports leading foundation model builders, technology companies, and enterprises to improve their AI performance across diverse applications from red teaming chatbots to domain-specific summarisation and reasoning.
Preference Ranking & Supervised Fine-Tuning for 70+ Dialects
Appen supported a global technology company in improving its LLM’s performance across more than 70+ dialects and 30+ languages by providing structured human feedback. Contributors engaged in multi-turn dialogues, ranking responses from five model variations based on coherence, factuality, fluency, and instruction-following. 250,000+ dialogue rows were collected, refining model outputs for supervised fine-tuning. The project expanded from 10+ dialects in 5+ languages to 70+ dialects, enhancing cultural alignment and language accuracy in model responses.
Multi-Domain Reasoning Prompts for LLM Fine-Tuning
Appen supported a leading LLM builder in developing complex, multi-domain prompts to enhance model reasoning capabilities. Using Appen's AI data platform (ADAP), contributors validated model outputs with AI Chat Feedback and Model Mate tools and provided step-by-step corrections across tasks requiring logical, statistical, and abstract reasoning. Appen delivered 10,000+ high-complexity prompts spanning 9 reasoning types and 10 domains, enabling targeted supervised fine-tuning that improved the model’s ability to tackle advanced reasoning tasks.
Fine-Tuning LLMs for Coding and Programming Tasks
To improve model performance on programming benchmarks, a foundation model builder partnered with Appen to fine-tune LLMs on diverse coding tasks such as NL2SQL, code review, and merge requests. Leveraging a dedicated team of 100+ coding experts, Appen created high-quality SFT data, developed benchmark sets, and ran A/B testing on each model iteration. This work led to measurable improvements in accuracy and relevance, helping the client achieve cutting-edge benchmark performance while reducing evaluation turnaround times through a continuous feedback loop.
ReflexAI: Supporting Veterans with Mental Health AI
Appen worked with ReflexAI to train and evaluate a mental health support chatbot for U.S. veterans. Our experts provided high-quality training data for supervised fine-tuning, simulating realistic dialogues and ensuring outputs were accurate, empathetic, and aligned with safety guidelines. This work helped improve access to trusted, AI-assisted mental health support for those who served.
Why Appen?
Appen enables supervised fine tuning with a network of global talent and AI Data Platform (ADAP) tooling designed for essential SFT tasks – like custom AI data collection, red teaming, and benchmarking – to ensure reliable and adaptable outputs for specialized use cases.
Domain Expertise
Fine tune your model for specialized domains, like engineering and law, with human-generated SFT data.
Human Alignment
Leverage Appen’s expert crowd to evaluate your model output, ensuring safe and reliable model performance across a range of languages, cultures, and applications.
Iterative Improvement
Develop efficient workflows for creating your SFT data, training your model, and validating performance with consistent, real-world testing.
AI Data Platform (ADAP)
Our popular AI data platform enforces efficient, high-quality, and guideline-compliant evaluation, benchmarking, A/B testing, and red teaming.
Ethical & Regulatory Compliance
Fine tune your model on ethically sourced, licensable data and human insights to mitigate risks to your business and end users.
Global Reach
Appen’s 1M+ global workforce ensures scalability, bridging the gap in multilingual AI to include rare and low-resource languages.
Get Started
With 25+ years of experience, Appen is the trusted data partner for 80% of leading LLM builders. Leverage Appen’s expert support to fine tune ethical, reliable AI solutions tailored to complex real-world challenges.