Use case
Complex & agentic reasoning
Generate and evaluate complex questions and responses to identify the best answer and provide logical reasoning.
Why Labelbox for complex reasoning
Increase data quality
Generate high-quality data by combining advanced tooling, humans, AI and on-demand services in a unified solution.
Accelerate time to value
Rapidly integrate data, create quality training data, and deploy models to production.
Access on-demand expertise
Highly-skilled labeling services, data science support, and industry insights available on-demand.
Collaborate in real-time
Enjoy direct access to internal and external labelers with real-time feedback on labels and quality via Labelbox platform.
Understanding complex and agentic reasoning
Complex and agentic reasoning is a paradigm shift in AI capabilities, enabling models to use step-by-step logic and execute actions towards an identified goal. This opens doors to AI systems that can make informed decisions, execute complex tasks, and better interact with the world.
The data challenge for advanced AI
Training AI models on complex and agentic reasoning requires a large, diverse dataset that captures the nuances of real-world scenarios and human decision-making processes. Without the right tools or experts, training data will be incomplete and unable to capture the logic behind each step of a complex decision.
Training complex reasoning with Labelbox
Labelbox empowers AI teams to train models that think, plan, and act intelligently. Our platform's flexibility and powerful annotation tools allow you to create tailored datasets that teach AI to understand natural language, set goals, reason through subtasks, and adapt to changing conditions.
Customer spotlight
A leading AI lab aimed to improve its large language model (LLM) for K-12 STEM education by identifying its weaknesses. Labelbox's Labeling Services, in collaboration with the Alignerr network, assembled a team of STEM experts with advanced degrees in fields like chemistry, biology, and engineering. These experts created multimodal prompts (text and image) and accurate answers to assess the model. Their work helped pinpoint the LLM’s limitations, enabling the lab to target areas for improvement.
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