AI Glossary

Operates autonomously to achieve complex goals by making independent decisions and executing multi-step tasks without constant human oversight. It combines the reasoning capabilities of large language models with traditional software logic to proactively plan, adapt to new information, and coordinate actions in real-time environments.

Standardizes how AI applications connect to external data sources, tools, and enterprise systems through an open-source framework. It functions as a universal connector, allowing large language models to securely access real-time information and perform tasks without requiring custom, one-off integrations for every individual data repository.

Guides generative AI models toward specific, high-quality outputs by designing and refining input instructions. This iterative process involves structuring context, constraints, and examples to bridge the gap between human intent and machine interpretation, ensuring the model produces accurate, relevant, and useful results for complex tasks.

Optimizes large language model outputs by connecting them to authoritative, external knowledge bases before generating responses. This process ensures that AI systems provide accurate, context-aware information by grounding answers in specific, trusted data sources rather than relying solely on pre-existing training parameters.

Build software applications by using natural language prompts to direct AI models, shifting the focus from manual syntax writing to high-level conceptual steering. This approach allows users to create functional programs by describing desired outcomes rather than writing individual lines of code.