RAG (Retrieval-Augmented Generation)
A technique where the AI retrieves relevant information from a knowledge base before generating a response, reducing hallucinations and grounding outputs in real data.
Why it matters
RAG is how AI answers questions about your specific documents instead of guessing from general training. It matters because it grounds responses in real, retrieved sources, which cuts down hallucinations and lets the AI cite where an answer came from. Most trustworthy "chat with your PDF" and company-knowledge assistants rely on it, and it's why they can stay current without retraining the whole model.
In practice
A company help bot using RAG doesn't invent your refund policy. When you ask, it first searches the official policy documents, pulls the relevant paragraph, and then writes its answer based on that exact text, often with a citation. That's the difference between a bot that confidently makes up rules and one you can actually rely on, because its answer is anchored to a real source.
Related terms
AI Dubbing
Automated translation and re-voicing of audio/video content into other languages while preserving the original speaker's voice characteristics, timing, and emotional delivery.
AI Orchestration
Coordinating multiple AI models, tools, and data sources in a unified pipeline. An orchestration layer manages prompt routing, context passing, error handling, and output aggregation across different AI services.
API (Application Programming Interface)
A way for software applications to communicate with each other. AI APIs let developers integrate AI capabilities into their own applications programmatically.
Attention Mechanism
A technique that allows AI models to focus on the most relevant parts of input data when generating output. In language models, attention determines which words in a sentence are most important for understanding each other word.
Diffusion Model
A type of generative AI that creates images by gradually removing noise from a random starting point. Powers tools like Stable Diffusion, DALL-E, and Midjourney. Works by learning the reverse of a noise-adding process.
Distillation
A technique where a smaller 'student' model learns to replicate the behavior of a larger 'teacher' model. Produces compact models that retain most of the teacher's capability while being faster, cheaper, and deployable on smaller devices.