Caching and Performance at Scale

Caching is the single most powerful tool for ensuring that your agentic application is Economically Viable. In a million-user system, 40% of queries are duplicates or near-duplicates. If you "Think" from scratch for every query, you are literally throwing money away.

In this lesson, we will learn how to implement Semantic Caching—a system that allows your agent to "Remember" the answer to a similar question from a different user half-way across the world.

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1. What is Semantic Caching?

Standard caching is "Exact Match" (e.g., if question == "A", return "B"). Semantic Caching is "Similarity Match."

1. User 1 asks: "How do I reset my password?" -> Agent Reasons -> Cache result.
2. User 2 asks: "What are the steps to change my password?" -> Cache says: "This is 95% similar to User 1's question." -> Return User 1's answer.

Performance Gain: Latency drops from 2s to 2ms. Cost drops to $0.

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2. Implementing the "Semantic Cache" with Redis

We use RedisVL (Redis Vector Library) or GPTCache.

1. The Hash: Every question is converted into a vector embedding.
2. The Lookup: We perform a vector search on our "Cache Index" before talking to the LLM.
3. The Threshold: If similarity > 0.98, we serve the cache. If < 0.98, we run the agent.

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3. Context Prefetching (Anticipation)

A high-performance agent "Predicts" what data it might need next.

• Scenario: A user asks "Tell me about your Pricing."
• Backend: While the agent is answering, a background thread Prefetches the data for "How to upgrade" and "Do you have a free trial?" into the Prompt Cache.
• The Result: When the user follows up with "Okay, how do I start a trial?", the answer is ready instantly.

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4. Prompt Caching (The LLM Level)

Newer APIs (like Anthropic Claude and DeepSeek) support Prompt Caching.

• If your system prompt (instructions + tools) is 5,000 tokens long, you usually pay for those 5,000 tokens every single time the user speaks.
• With Prompt Caching: You pay once. If the next message arrives within 5 minutes, those 5,000 tokens are "Free" or "Discounted."
• Impact: In long conversations, this can reduce costs by 80-90%.

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5. Cleaning the Cache: "Semantic Invalidation"

Caching is dangerous if your data changes frequently.

• User asks: "What is the status of my order?"
• The Problem: If the order status was "Shipped" 5 minutes ago, but it's "Delivered" now, a semantic cache will give the wrong answer.
• Rule: Never cache queries that involve Private User Data or Real-time API data.

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6. Real-World Trade-Off: Precision vs Recall

Strategy	Performance	Risk
No Cache	🔴 Slow	🟢 Accurate
Exact Match	🟡 Moderate	🟢 Accurate
Semantic (0.95)	🟢 Fast	🟡 Slight Risk of Wrong Nuance
Semantic (0.80)	🔵 Ultra Fast	🔴 High Risk of Irrelevant Answers

Production Setup: Use a high threshold (0.97) and always provide a "Refresh" button in the UI (Module 9.1).

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Summary and Mental Model

Think of Caching like A Teacher's FAQ.

• If every student asks "When is the exam?", the teacher gets tired and slow (Rate limits).
• If the teacher writes the date on the Blackboard (The Cache), the students can find the answer instantly without bothering the teacher.

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Exercise: Performance Optimization

1. Threshold Testing: You have two questions:
   • "Can I cancel my subscription?"
   • "Can I pause my subscription?"
   • These are semantically similar (>0.90 match). Should you use the same cached answer for both? Why/Why Not?
2. Invalidation: Your company just updated the "Terms of Service."
   • Describe how you would "Clear" the semantic cache for any questions related to the TOS.
3. Budgeting: If you implement Prompt Caching (Lesson 4), how would you change your "Token Limit" guardrails from Module 16.3? Ready for the grand finale? Next module: Ethical AI and Governance.