40% of FAANG companies trust Sama to deliver industry-leading data that powers AI
Our dedicated Gen AI team will help you fine tune an existing LLM based on your unique objectives.
Sama’s supervised fine-tuning projects start with tailored consultations to understand requirements for model behavior. This collaborative effort involves identifying key characteristics like tone, terminology, writing styles, relevant factual knowledge and more. We’ll align on how you want your model to behave and set targets across a variety of dimensions.
Our AI specialists leverage their expertise to write high quality prompts along with corresponding answers across varying formats and dimensions. We’ll curate a highly specialized set of data to help streamline the LLM development process.
After an initial set of data has been created we’ll work with your team to review the prompts and responses created to ensure the data aligns with the intended purpose of the generative model or LLM. If needed, our teams will collaborate closely to recalibrate.
As errors in model outputs are identified, our team will begin creating an additional training data set that can be used to fine-tune model performance based on your objective: domain specificity, task optimization, etc. This new data consists of rewritten prompts and corresponding responses that address the specific mistakes made by the model.
When the project is complete, we follow a structured delivery process to ensure smooth integration with your LLM training pipeline. We offer flexible and customizable delivery formats, APIs, and the option for custom API integrations to support rapid development of models.
With over 15 years of industry experience, Sama’s data annotation and validation solutions help you build more accurate GenAI models and LLMs—faster.
Our team will help you build upon an existing LLM to create a proprietary model tailored to your specific needs. We’ll craft new prompts and responses, evaluate model outputs, and rewrite responses to improve accuracy and context optimization.
Our human-in-the-loop approach drives data-rich model improvements & RAG embedding enhancements through a variety of validation solutions. Our team provides iterative human feedback loops that score and rank prompts along with evaluating outputs. We also provide multi-modal captioning and sentiment analysis solutions to help models develop a nuanced understanding of user emotion and feedback.
We’ll help create new data sets that can be used to train or fine tune models to augment performance. If your model struggles with areas such as open Q&A, summarization or knowledge research, our team will help create unique, logical examples that can be used to train your model. We can also validate and reannotate poor model responses to create additional datasets for training.
Our team of highly trained of ML engineers and applied data scientists crafts prompts designed to trick or exploit your model’s weaknesses. They also help expose vulnerabilities, including generating biased content, spreading misinformation, producing harmful outputs and more to improve the safety and reliability of your Gen AI models. This includes large scale testing, fairness evaluation, privacy assessments and compliance.
Our team is trained to provide comprehensive support across various modalities including text, image, and voice search applications. We help improve model accuracy and performance through a variety of solutions.
Our proactive approach minimizes delays while maintaining quality to help teams and models hit their milestones. All of our solutions are backed by SamaAssure™, the industry’s highest quality guarantee for Generative AI.
SamaIQ™ combines the expertise of the industry’s best specialists with deep industry knowledge and proprietary algorithms to deliver faster insights and reduce the likelihood of unwanted biases and other privacy or compliance vulnerabilities.
SamaHub™, our collaborative project space, is designed for enhanced communication. GenAI and LLM clients have access to collaboration workflows, self-service sampling and complete reporting to track their project’s progress.
We offer a variety of integration options, including APIs, CLIs, and webhooks that allow you to seamlessly connect our platform to your existing workflows. The Sama API is a powerful tool that allows you to programmatically query the status of projects, post new tasks to be done, receive results automatically, and more.
First batch client acceptance rate across 10B points per month
Get models to market 3x faster by eliminating delays, missed deadlines and excessive rework
Lives impacted to date thanks to our purpose-driven business model
2024 Customer Satisfaction (CSAT) score and an NPS of 64
Learn more about Sama's work with data curation
To ensure effective and responsible implementation of Gen AI, financial institutions must navigate challenges such as model explainability, data privacy, and regulatory compliance. By understanding the tech’s potential and the strategies for overcoming associated risks, you can position your organization for a competitive advantage in the age of intelligent automation. Here are four key things to consider.
Supervised fine-tuning takes a pre-trained LLM with general language knowledge and undergoes targeted improvement for a specific task. This involves feeding the LLM a curated dataset of labeled examples that connect desired outputs with the corresponding input data. By analyzing these labeled pairs, the LLM refines its understanding of the task and learns the specific patterns that lead to the desired outcome. This process essentially tailors the LLM's strengths to a particular domain, making it a more effective tool for that specific use case.
High-quality data, free from biases and factual errors, leads to more accurate and reliable outputs from the generative model. Additionally, a diverse range of training data, encompassing various styles, formats and viewpoints, equips the model to handle a wider range of prompts and scenarios effectively. This allows the new model to grasp the intricate connections between language patterns and desired outcomes, ultimately leading to a more accurate and effective fine-tuned model.
Prompt engineering is the process of creating the contextual instructions for your generative AI model. Prompt engineers don't just write generic instructions; they consider the model's capabilities, the specific task at hand, and the intended outcome. They might use clear and concise language, provide specific examples, or even break down complex tasks into smaller, easier-to-understand prompts.
Contextual optimization is crucial for supervised fine-tuning of LLMs because it bridges the gap between the general knowledge of the pre-trained model and the specific demands of the fine-tuning task. LLMs pre-trained on massive datasets can struggle to adapt to specific tasks without additional context. Contextual optimization provides that context by focusing the LLM on the nuances of the training data relevant to the task. This allows the fine-tuned model to perform well not just on the specific examples it saw during training, but also on unseen data that shares similar context.