Prompt Caching Explained: Why You Should Not Pay Twice to Read the Same Page

Business Pill · Why you should not pay twice to read the same page

Prompt caching stores the part of a request that repeats, so the model does not process it again. The repeated part costs a fraction of the price, but only if it comes first and stays the same.

A short explainer video, under a minute. The robot barista and its handbook are an illustration, and the video gives no actual price or discount.

At a glance

The Short Answer

Picture a robot barista that reads the entire shop handbook before taking each order. It is forty pages, and it reads them every single time the queue reaches the door.

Then someone points out the obvious: the handbook has not changed since the last customer. So the barista keeps it open and reads only the new order.

What Prompt Caching Is

That is called prompt caching. It means storing the part of a request that repeats, so the model does not process it again.

The repeated part now costs a fraction of the price, and the answer starts sooner. The video gives no actual price or discount, so read this as the shape of the saving, not a number.

The Two Catches

The first catch is that the cache is forgotten after a short while. The video does not say how long.

The second is that it only works if the repeated part comes first and stays exactly the same.

Why It Matters

The video turns this into one question: what do we send every time, unchanged?

Do not pay twice to read the same page. Put what repeats first, and keep it still.

Where This Shows Up in AI Business Today

The Business Engineer Lens

Where this idea sits in the Business Engineer library:

Related Business Pills

All Business Pills

• The Memory Wall: Why a Faster AI Chip Is Not Faster AI
• Tokens per Watt: What an AI Data Centre Actually Produces
• Why AI Can’t Be Both Instant and Cheap: Latency vs Throughput
• The Model and the Harness: Why Same-Model Products Differ
• Context, Not Capability: Why a Smart AI Model Gives Poor Answers
• Discardable Software: When Code Is Cheap Enough to Throw Away
• Human in the Loop vs Human on the Loop: Supervising AI Agents
• The AI Audit Problem: When Making Work Is Cheaper Than Checking It
• AI Evaluation as Acceptance Test: How to Know It’s Good Enough
• Extensibility Is Control: Who Holds the Power in an AI Product
• RLHF Explained: How an AI Model Learns What People Prefer
• Pretraining Explained: How an AI Model Learns Before Anyone Teaches It
• Fine-Tuning Explained: How to Adapt a General AI Model to One Job
• Tokens Explained: The Unit AI Reads, Writes and Bills In
• Embeddings Explained: How a Machine Compares Meaning
• RAG Explained: How a Model Answers From Your Documents
• AI Hallucination Explained: Why Models State False Things
• Distillation Explained: How a Small Model Learns From a Large One
• Reasoning Models Explained: What Changes When AI Thinks First
• Tool Use Explained: How an AI Model Goes From Text to Action
• Capex and Depreciation Explained: Why Chip Lifetime Drives AI Profits
• Run-Rate Revenue Explained: What an AI Company’s Number Means
• Backlog Explained: Revenue That Is Signed but Not Yet Earned
• Switching Costs Explained: Why It Is Hard to Leave an AI Supplier
• Temperature Explained: The Dial That Sets How Predictable an AI Model Is
• Guardrails Explained: Rules Enforced Around an AI Model, Not Inside It
• Benchmarks Explained: Why a Test Score Is Not Your Own Result
• Agent Memory Explained: How an AI Assistant Remembers You Between Chats
• MCP Explained: The Shared Plug That Connects AI Models to Tools
• Quantization Explained: How a Large AI Model Is Shrunk to Fit
• Parameters Explained: Where an AI Model’s Knowledge Lives
• Scaling Laws Explained: Why AI Labs Keep Building Bigger
• Synthetic Data Explained: When a Model Writes Its Own Training Examples
• Transformers and Attention Explained: How AI Reads Every Word at Once