Prompt Caching

• Key points are in the visual diagram above.

Understanding Prompt Caching helps you build reliable, scalable agent applications instead of fragile demos.

Think of Prompt Caching as a specialized capability in your Agent Runtime & Production engineering toolkit.

Scenario

A production team in Agent Runtime & Production uses Prompt Caching to handle a real user request — reducing manual work and improving response quality with proper validation and logging.

Solution

In Agent Runtime & Production, apply Prompt Caching to this scenario: A production team in Agent Runtime & Production uses Prompt Caching to handle a real user request — reducing manual work and improving response quality with proper validation and logging. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Do this before moving to the next module — reading alone is not enough.

Open the Code Walkthrough below and run it locally. Change one parameter related to Prompt Caching (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Highlighted lines show where Prompt Caching happens in the code.

1# Prompt Caching — minimal example2from openai import OpenAI3
4client = OpenAI()  # create API client5
6# Ask the model to explain this topic7response = client.chat.completions.create(  # core API call for Prompt Caching8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You explain prompt caching clearly."},11        {"role": "user", "content": f"What is prompt caching?"},12    ],13    temperature=0,14)15print(response.choices[0].message.content)  # show output for debugging

• Skipping evaluation for Prompt Caching before production
• No logging or tracing around prompt caching steps
• Ignoring cost and latency implications

Quick recap — the most important points from this module.