Caching strategies for LLMs in Prompt Engineering / GenAI - Full Explanation

Introduction

When using large language models (LLMs), responses can take time and computing power. Caching helps by saving answers to repeated questions, so the model doesn't have to work from scratch every time.

Explanation

Response Caching

This strategy saves the exact answers the LLM gives for specific inputs. When the same input appears again, the saved answer is reused instantly without asking the model again.

Response caching speeds up repeated queries by reusing previous answers.

Partial Output Caching

Instead of saving the full answer, this method stores parts of the output or intermediate results. It helps when answers share common sections, allowing reuse of those parts to build new responses faster.

Partial output caching saves time by reusing shared parts of answers.

Context Window Caching

LLMs use a limited context window to understand input. This strategy caches processed context chunks so the model can quickly recall relevant information without reprocessing everything.

Caching context chunks reduces repeated processing of input history.

Embedding Caching

Embeddings are numeric summaries of text used by LLMs to understand meaning. Caching embeddings for common inputs avoids recalculating them, speeding up similarity searches or related tasks.

Embedding caching saves time by reusing computed text summaries.

Real World Analogy

Imagine a busy coffee shop where customers often order the same drinks. Instead of making each drink from scratch every time, the barista keeps some popular drinks ready or remembers how to quickly prepare them. This saves time and keeps customers happy.

Response Caching → Serving a pre-made popular coffee instantly when a customer orders it again

Partial Output Caching → Using pre-prepared coffee shots or milk foam that can be combined to make different drinks faster

Context Window Caching → Remembering recent orders to quickly prepare similar drinks without starting from zero

Embedding Caching → Having a recipe book with summaries of popular drinks to quickly find how to make them

Diagram

Diagram

┌─────────────────────────────┐
│        User Input            │
└─────────────┬───────────────┘
              │
     ┌────────▼────────┐
     │ Check Response   │
     │ Cache           │
     └───────┬─────────┘
             │ Yes
             ▼
   ┌───────────────────┐
   │ Return Cached      │
   │ Response          │
   └───────────────────┘
             │ No
             ▼
   ┌───────────────────┐
   │ Process Input     │
   │ (Context, Embeds) │
   └───────┬───────────┘
           │
   ┌───────▼───────────┐
   │ Generate Response │
   └───────┬───────────┘
           │
   ┌───────▼───────────┐
   │ Cache Results     │
   └───────┬───────────┘
           │
           ▼
   ┌───────────────────┐
   │ Return Response   │
   └───────────────────┘

This diagram shows how caching checks for saved answers before processing input and generating new responses.

Key Facts

Response Caching → Stores full answers to reuse for identical inputs.

Partial Output Caching → Saves parts of answers to build new responses faster.

Context Window → The limited amount of recent input the LLM can consider at once.

Embedding → A numeric summary representing the meaning of text.

Caching → Saving data temporarily to speed up future access.

Common Confusions

Caching means storing every possible answer in advance.

Caching means storing every possible answer in advance. Caching only saves answers or parts for inputs that have already been processed, not all possible questions.

Cached responses are always perfect and up-to-date.

Cached responses are always perfect and up-to-date. Cached answers may become outdated if the model or data changes, so caches need refreshing.

Embedding caching stores the full text input.

Embedding caching stores the full text input. Embedding caching stores numeric summaries, not the original text.

Summary

Caching saves time by reusing previous answers or parts of answers instead of generating them again.

Different caching strategies focus on full responses, partial outputs, context chunks, or embeddings.

Effective caching improves speed and reduces computing costs when using large language models.