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Our team of ML engineers and applied data scientists craft prompts designed to trick or exploit your model’s weaknesses. We will help you map the vulnerabilities of your AI systems so you can improve the safety and reliability of your generative models.
Sama’s red teaming projects start with tailored consultations to understand requirements for model performance. We believe the only way red teaming brings value is if it takes into account your context and assumptions about your models in order to set the right targets around threats that matter most to you.
Our team of ML engineers and applied data scientists meticulously craft a plan to systematically expose vulnerabilities. We’ll produce an initial vulnerability map, then prioritize with you what areas are most critical.
When vulnerabilities are exposed, our team will create and test more prompts around these areas to see how the model reacts. We’ll come up with similar examples along with using models to create variants of human generated prompts to create large-scale tests.
Documenting the space of vulnerabilities identified is key to track the evolution over time. Our teams will produce a complete log of the vulnerabilities identified, and methods used to identify them. Our goal is to facilitate retrieval and comparison to this as your model evolves.
Red teaming is not an audit. It's an iterative journey to make sure your models are compliant, safe and reliable. Our teams are equipped to continue vulnerability testing following the evolution of your needs.
Our team includes ML engineers, applied scientists, and human-AI interaction designers. Their experience spans domains including natural language processing (NLP) and computer vision (CV), and they have worked with models across several different industries, including automotive, robotics, e-commerce, bioinformatics, and finance.
At Sama, we focus on providing our clients with cutting-edge, actionable advice for improving training data quality and testing LLMs. We also practice what we preach: applying what we learn from testing models to our own internal annotation operations, ensuring that we maintain our industry-leading quality.
With over 15 years of industry experience, Sama’s data annotation and validation solutions help you build more accurate GenAI 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 us 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
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.
Red teaming for generative AI and LLMs is when human experts, such as our ML engineers and applied data scientists, act as adversarial testers, crafting prompts and scenarios designed to trip up the AI. They might introduce subtle biases, nonsensical data, or even deepfakes to see if the model generates inaccurate or misleading outputs. The goal is to find blind spots in the model’s thinking that could lead to biased, inaccurate, or misleading outputs.
Red teaming strengthens the overall robustness of the model, making it less susceptible to manipulation and misuse in the real world. By simulating real-world misuse scenarios, red teaming uncovers hidden biases and vulnerabilities the model might not encounter during standard training. It helps improve model accuracy by identifying situations where the model might generate unreliable or fabricated information as well as enhances model safety by catching potential issues like the generation of harmful content.
Unlike traditional cybersecurity red teaming where goals are clear (e.g., breach a system), generative AI red teaming lacks a universal objective. What constitutes a successful "attack" can vary depending on the model's purpose. Additionally, crafting effective adversarial prompts requires a deep understanding of the model's inner workings and potential biases.
The duration of a red teaming engagement for generative AI can vary significantly depending on several factors. The complexity of the model, the desired scope of the testing, and the resources available all play a role. Simpler models with well-defined purposes might undergo a red teaming exercise lasting just a few weeks while intricate models that require in-depth testing for various biases and safety concerns could take months. Red teaming also can and should be an ongoing process with periodic checks to ensure the model you have in production is tested against the latest in Gen AI red teaming best practice.
Red teaming for generative AI can be partially automated, but human expertise remains crucial. While automation can be used for tasks like generating basic prompts or running repetitive tests, crafting truly effective adversarial prompts requires a deep understanding of the model's architecture, potential biases, and the desired areas of vulnerability testing. This nuanced knowledge is best wielded by human red teamers who can adapt their strategies based on the model's responses and identify subtle weaknesses that might be missed by automated scripts.