Introduction to Prompt Caching Beginner

Prompt caching allows AI providers to reuse previously processed prompt content, dramatically reducing both cost and latency. If your application sends similar prompts repeatedly, caching can cut your API bill by up to 90%.

What is Prompt Caching?

When you send a prompt to an AI model, the provider must process every input token through the model's layers. This processing is the primary cost driver. Prompt caching stores the intermediate computation results (KV cache) for prompt prefixes so that subsequent requests sharing the same prefix can skip re-processing those tokens.

Key Insight: Prompt caching does not change the model's output. It is purely an optimization that avoids redundant computation. The same prompt produces the same results whether cached or not.

How It Works (Simplified)

Conceptual 
# Request 1: Full processing
[System Prompt: 2000 tokens] + [User Message: 100 tokens]
  → Process all 2100 tokens (full cost)

# Request 2: Same system prompt, different user message
[System Prompt: 2000 tokens] + [User Message: 150 tokens]
  → Reuse cached 2000 tokens + process 150 new tokens
  → Pay reduced rate for cached tokens (up to 90% off)

Why Prompt Caching Matters

💰

Cost Savings

Cached tokens cost 10-50% of regular input token pricing depending on the provider. For apps with long system prompts, savings are substantial.

⚡

Lower Latency

Skipping computation for cached tokens reduces time-to-first-token (TTFT), making your application feel more responsive to users.

📈

Longer Context

With cheaper input tokens, you can afford to include more context (documentation, examples, conversation history) in each request.

🎯

Better UX

Faster responses and the ability to include richer context directly improve the quality and speed of AI-powered features.

Common Use Cases

Use Case Cacheable Content Potential Savings
Chatbots with system prompts System instructions, persona, rules 60-80%
RAG applications Retrieved documents, base instructions 40-70%
Code assistants Repository context, coding standards 70-90%
Multi-turn conversations Conversation history prefix 50-80%
Batch processing Shared instructions across items 80-90%

Caching Approaches by Provider

Two Main Approaches:
  • Explicit caching (Anthropic): You mark which content blocks should be cached using cache_control parameters. Gives you precise control.
  • Automatic caching (OpenAI): The provider automatically caches prompt prefixes above a minimum length. No code changes needed, but less control.

We will explore both approaches in detail in the next two lessons.

← Course Overview Anthropic Caching →

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