The Importance of Caching LLM Calls
Understand the economic and performance benefits of caching LLM responses and embedding lookups in production.
What is Caching?
Imagine you look up a word in a dictionary. If you need to look up the same word again, it's faster to remember it than to open the dictionary and find it again.
Caching is like remembering. It stores results of expensive operations so you can reuse them quickly instead of re-doing the work.
LLM Calls: Not Free
Large Language Models (LLMs) often charge per "token" used. Every time your application sends a prompt and receives a response, you pay for the tokens.
- Prompt tokens: The text you send to the LLM.
- Completion tokens: The text the LLM generates back.
Repeatedly asking the same question means repeatedly paying for the same work.
All lessons in this course
- The Importance of Caching LLM Calls
- In-Memory and External Caching Strategies
- Integrating Caching into a RAG Pipeline
- Semantic Caching for LLM Apps