Prompt Caching
A performance optimization that stores and reuses the processed representation of repeated prompt prefixes. Reduces latency and cost when sending similar prompts — especially useful for system prompts and few-shot examples.
Why it matters
Many AI apps send the same big chunk of text over and over, like a long set of instructions or a reference document, in front of every user question. Prompt caching lets the model remember that processed prefix instead of reworking it each time. The result is noticeably faster responses and lower bills, which matters a lot for chatbots and tools that reuse the same background material constantly.
In practice
A coding assistant includes your project's style guide and key files at the start of every request. Without caching, the model reprocesses all of that each time you type. With prompt caching, it stores the processed version of that fixed prefix, so only your new question needs fresh work. Replies come back quicker and each call costs less, which adds up fast over a full day of coding.
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.