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We’ll work with your internal specialists and domain experts to understand your requirements. Then we’ll align on prompt distribution targets including various dimensions such as tone, delivery format, justification and more.
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 model 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.
Once an initial set of prompts and responses has been created. Our team will scale the process by coming up with multiple variations of prompts to augment your training data. We’ll also use proprietary models to help create variants of human generated prompts to create large-scale tests.
When training data is complete, we follow a structured delivery process to ensure smooth integration with your generative model or 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 and LLMs—faster.
Our data experts will review your model’s responses for accuracy, identify and highlight any errors, and rewrite responses to improve model performance, combining workflow automation with our human-in-the-loop approach to ensure speed and quality.
Our team can assess how well your Gen AI model understands, interprets, and executes instructions. We’ll help you identify where your model doesn’t comply, including why a response was selected. Any issues are highlighted and flagged, making it easier and more efficient to fine-tune.
Sama’s highly trained team of experts can help you improve the quality and alignment of model outputs through feedback loops, RLHF, and more. With domain expertise across multiple industries and functions, we can analyze and rank model responses, indicate the rationale behind each choice, and highlight any issues within the outputs.
Sama can help you scale captioning for a variety of modalities. Our team of experts will describe the content of visual inputs, verify if the captions match, and rewrite captions as needed to retrain the model to reduce errors and hallucinations. Sama’s proprietary platform makes sampling easy and our collaborative workflows help reduce subjectivity and ambiguity from project kickoff.
With domain expertise across a variety of industries and functions, Sama’s dedicated team can create new prompts and responses based on your model goals. We can also rewrite responses, tailored to model capabilities and limitations, to augment existing training data. Our team can also employ chain of thought to provide clear rationale for chosen outputs.
When real training data is too difficult or not cost effective to obtain, our team can create synthetic data sets to help train your model, using a human-in-the-loop approach to ensure the highest level of quality. Our team will define objectives for your data, including a specific domain or other required parameters, and test outputs for quality and accuracy by comparing them against outputs from authentic data.
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
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2024 Customer Satisfaction (CSAT) score and an NPS of 64
Learn more about Sama's work with data curation
At Sama, we’ve developed tools that streamline this data curation process, ensuring every selected data sample aligns with your goals. Given the need for rapid experimentation and frequent configuration adjustments in our data curation pipeline - typically handled by an ML Applied Scientist - we leverage the Valohai platform to boost efficiency and reduce costs, all without requiring DevOps support.
This data serves as the model's learning material, allowing it to grasp the underlying patterns, structures, and relationships within that information. Essentially, training data is the foundation upon which a generative AI model builds its understanding of the world, shaping its ability to generate creative text formats, translate languages, or produce realistic images.
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.
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.
By incorporating edge cases into training data and testing procedures, developers can identify these potential pitfalls. This can involve feeding the model nonsensical prompts, introducing data with deliberate errors or simulating unusual user interactions. By exposing the model to these edge cases, developers can refine its ability to handle unexpected situations, leading to more adaptable and reliable AI.