Caching: Engineering for Vibe Coders
Vibe coders often build prototypes that work correctly but feel slow, expensive, or unresponsive. The issue is often repeated work: calling the same APIs, re-running the same AI prompts, or recalculating the same results. Caching is the practice of storing results so you can reuse them instead of recomputing them every time.
Thinking about caching before you start coding helps your prototype feel fast, reduce costs, and avoid unnecessary complexity later.
1. What is caching?
Caching is storing the result of an expensive operation so it can be reused later.
Examples:
- Saving an AI model response instead of re-running the prompt
- Storing API responses instead of calling the API repeatedly
- Remembering computed results instead of recalculating them
Caching improves performance by trading a small amount of storage for faster responses.
🟢 Pre-prototype habit:
List which operations in your prototype are slow, expensive, or repeated. These are prime candidates for caching.
2. Why caching matters for AI prototypes
AI prototypes are especially sensitive to repeated work:
- LLM calls can be slow and costly
- External APIs may have rate limits
- Recomputing the same results wastes time and money
- Latency ruins user experience even in demos
Caching allows your prototype to feel responsive and stable even under repeated use.
🟢 Pre-prototype habit:
Identify which AI calls or API requests are safe to reuse. Decide which results can be cached without harming correctness.
3. What should and should not be cached
Not everything should be cached.
Good caching candidates:
- Read-only data
- Deterministic AI outputs for the same input
- Reference data that changes infrequently
Poor caching candidates:
- Highly dynamic or real-time data
- User-specific sensitive data without isolation
- Actions with side effects
Caching the wrong thing can cause stale data, confusing behavior, or security issues.
🟢 Pre-prototype habit:
For each candidate, ask: “If this result is reused, will it still be correct?” Write down acceptable staleness, if any.
4. Cache invalidation and freshness
One of the hardest parts of caching is knowing when to refresh cached data.
Common strategies:
- Time-based expiration (TTL)
- Manual invalidation when data changes
- Version-based keys for different logic paths
- Clearing cache on deploy or restart for prototypes
Without invalidation, your prototype may serve outdated or misleading results.
🟢 Pre-prototype habit:
Decide how long cached data can live and what events should invalidate it. Even a simple TTL is better than none.
5. Lightweight caching strategies for prototypes
You do not need enterprise caching systems to start.
Simple approaches:
- In-memory dictionaries or maps
- File-based caching for local prototypes
- Basic key-value stores
- Framework-provided caching utilities
Start simple and evolve only when needed.
🟢 Pre-prototype habit:
Choose the simplest caching mechanism that works for your prototype. Plan how it can be replaced later if needed.
6. Quick pre-prototype checklist
| Checklist Item | Why It Matters |
|---|---|
| Identify slow or repeated operations | Reveals caching opportunities |
| Decide what data is safe to cache | Prevents incorrect behavior |
| Define acceptable staleness | Avoids surprises |
| Plan cache invalidation | Keeps data fresh |
| Choose a simple caching mechanism | Reduces early complexity |
Closing note
Caching is one of the easiest ways to dramatically improve the performance and usability of AI prototypes. When planned early, it reduces latency, lowers costs, and avoids major refactors later.
🟢 Pre-prototype habit:
Before coding, list repeated operations, decide what can be cached, define freshness rules, and choose a simple caching strategy. Early planning makes your prototype faster without making it fragile.
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