
What is Teammate Lang
Teammately is an advanced AI Agent designed to autonomously build, refine, and optimize AI products, models, and agents. It leverages a scientific approach to AI development, combining techniques like LLM, Prompt Engineering, RAG, and ML to create high-quality AI solutions. The platform focuses on reducing hallucinations, quantifying AI capabilities, and iterating towards user-defined objectives, allowing human AI engineers to focus on more creative and strategic roles.
How to Use Teammate Lang
- Begin by aligning your objectives through Product Requirement Docs (PRD).
- Teammately AI Agents will use these objectives to build, evaluate, and refine your AI.
- Utilize features like multi-step prompting, serverless vector search, and dynamic LLM-as-a-judge to enhance your AI's performance.
- Deploy your AI globally via a single API and monitor its performance post-production.
Use Cases of Teammate Lang
Teammately is ideal for AI engineers and teams looking to automate the development and refinement of AI models. It is particularly useful for projects requiring high-quality outputs, minimal hallucinations, and iterative improvements. The platform is also beneficial for teams with limited resources, as it automates many aspects of AI development, including feasibility studies, model testing, and interpretability.
Features of Teammate Lang
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Scientific AI Development
Teammately employs a scientific approach to refine and select the best combination of prompts, foundation models, and knowledge chunking.
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Hallucination Reduction
The platform quantifies AI capabilities and synthesizes fair test datasets to minimize hallucinations and ensure reliable outputs.
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Objective Alignment
Teammately begins with aligning objectives through PRD, which guides the AI Agents in building, evaluating, and refining your AI.
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Multi-step Prompting
Supports advanced prompting techniques like Chain-of-Thought, ReAct, and Query Optimization before Retrieval.
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Serverless Vector Search
Enables search and retrieval without the need for complex infrastructure setup.
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Dynamic LLM-as-a-judge
Quantifies qualitative outputs by scoring natural language responses consistently at scale.
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Deep Iteration
Allows AI to infinitely refine your models until they meet your goals.
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Feasibility Study
Provides a rationale for the feasibility of your AI ideas before you invest in speculative technology.
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Auto Test New Models
Tests new models against your data to ensure improvements before deployment.
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Interpretability
Detects misbehavior and interprets potential causes to minimize resolution time.
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Neural Mesh
Dynamically connects and routes generative, RAG, and model modules for optimal results.