Tutorials Prompt Engineering Tutorial
AI Caching — Complete Guide
AI Caching — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of Prompt Engineering Tutorial on Toolliyo Academy.
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Prompt Engineering Tutorial · Lesson 83 of 100
AI Caching
Prompts ✓ → Apps
Apps · 2 — RAG & agents · ~10 min · Module 9: Performance & Optimization
What is this?
AI caching stores LLM responses (or embeddings) keyed by prompt hash — identical requests skip the model entirely.
Why should you care?
PromptVerse edge cache hits FAQ classify prompts thousands of times daily with zero LLM latency.
See it live — copy this example
Copy the prompt into ChatGPT, Claude, or your LLM API playground and compare outputs.
key = sha256(model + prompt_version + normalized_user_text)
if (cache.has(key)) return cache.get(key)
out = await llm(...)
cache.set(key, out, ttl=3600)What happened?
- Hash includes prompt_version so deploy invalidates stale answers.
- TTL balances freshness vs savings.
- normalized_user_text ignores case/spaces.
Practice next
- Hash two identical prompts — same key.
- Bump prompt_version — new key.
- Measure hit rate on classify endpoint.
- Cache embeddings separately from completions.
- Do not cache high-risk personalized medical advice.
Remember
Cache by prompt hash + version. TTL by content type. Invalidate on prompt deploy.
FAQ thundering herd
Same password question 5000×/day.
Outcome: 94% cache hit; p50 latency 12ms.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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