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.
A concrete example
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.
How to use it
When many requests share a long fixed prefix — a system prompt, a style guide, a document everyone asks questions about — caching that prefix cuts both cost and latency substantially. Getting the benefit means structuring prompts so the stable part comes first and the varying part last, which is worth doing deliberately rather than by accident.
The common mistake
Putting the variable part at the top. A prefix only matches while it is identical, so a timestamp or a user name at the beginning invalidates the cache for every request and the saving silently never appears.
Related terms
KV Cache
Key-Value cache stores intermediate attention computations during text generation, avoiding redundant recalculation for previously processed tokens. Dramatically speeds up long-form generation and multi-turn conversations.
Inference Cost
The computational expense of running a trained AI model to generate outputs. Measured in cost per token or per request. Varies dramatically between models — GPT-4 class models cost 10-50x more per token than smaller models.
System Prompt
A special instruction set given to an AI model before the user's message, defining the model's persona, behavior rules, output format, and constraints. The foundation of custom AI assistants and chatbots.
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.