System DesignAI assistants can generate caching code quickly, suggest reasonable TTLs, and help you think through which layers to use. But caching has consistent AI blind spots that will bite you if you do not compensate for them.
What AI does well vs. what it gets wrong
| Task | AI quality | Why |
|---|---|---|
| Generate cache-aside boilerplate | Excellent | Mechanical pattern, well-documented |
| Suggest what data to cache | Good (with context) | Reasonable defaults for common scenarios |
| Estimate TTL values | Decent starting point | Suggests common values, but cannot know your actual traffic |
| Design invalidation strategy | Weak | Underestimates the coordination complexity |
| Identify stampede risk | Poor | Rarely considers concurrent access unless asked |
| Evaluate failure scenarios | Poor | Tends to assume happy path, ignores cache crashes and split-brain |
| Choose eviction policy | Good | Well-documented tradeoffs, mechanical decision |
| Design cache key schema | Good | Pattern-based, few edge cases |
Effective prompts for cache design
Prompt 1: Initial cache strategy
This works because it gives AI concrete constraints: data volume, traffic pattern, latencyWhat is latency?The time delay between sending a request and receiving the first byte of the response, usually measured in milliseconds. requirement, and freshness tolerance.
Prompt 2: Identifying what to cache
AI reasons about specific queries much better than abstract data models.
Prompt 3: Estimating cache hit rates
AI is good at back-of-envelope calculations, but always sanity-check against real access patterns.
What to verify in AI-generated cache designs
1. Over-caching
AI often suggests caching data that does not need it. A query that takes 2ms and runs 10 times per second does not need a cache, the overhead of serializing, storing, and invalidating the cache entry might cost more than the query itself. Always measure your baseline before adding a cache layer. Red flag: AI suggests caching without asking how fast the query already is.
2. Ignoring invalidation complexity
AI often says "set a 5-minute TTLWhat is ttl?Time-to-Live - a countdown attached to cached data that automatically expires it after a set number of seconds." for data where that is insufficient. If a user updates their profile and immediately views it, a 5-minute TTL means they see stale data. AI rarely volunteers this problem unless you ask. What to ask: "What happens when a user writes data and immediately reads it back? Will they see their own update?"
3. Underestimating stampede risk
AI almost never mentions cache stampede unprompted. It will give you a clean cache-aside implementation with no stampede protection. What to ask: "This endpointWhat is endpoint?A specific URL path on a server that handles a particular type of request, like GET /api/users. gets 10,000 req/s. What happens when the cache key expires? How do we prevent a thundering herd?"
4. Happy-path thinking
AI designs for the success case. It does not naturally consider: What if Redis goes down? What if the cache is full and evicting aggressively? What if a deploy flushes the entire cache? What if a bug writes bad data to the cache? What to ask: "What happens if Redis is unavailable for 5 minutes? What is the fallback?"
A hybrid workflow for cache design
- Describe your system to AI with concrete numbers (traffic, data size, latencyWhat is latency?The time delay between sending a request and receiving the first byte of the response, usually measured in milliseconds. targets, freshness requirements). Get an initial strategy.
- Challenge the invalidation plan. Ask about write-then-read consistency, stampede scenarios, and failure modes. AI will often revise significantly.
- Validate the numbers. Use AI to calculate expected memory usage and hit rates. Compare against actual monitoring data after deployment.
- Generate the implementation code with AI. Cache-aside and write-through patterns are mechanical, AI produces clean code for these.
- Write the failure handling yourself. Circuit breakers, fallback-to-database, stampede protection, these require understanding your specific failure modes.
- Load test before trusting it. Simulate cache misses, key expiration, and Redis restarts under realistic traffic.
Let AI handle the mechanical parts (code generation, initial design, calculations) and own the architectural decisions yourself (invalidation strategy, failure modes, consistency guarantees).
cache:products:${userId}:${productId} but the product page is the same for all users, every user gets their own cached copy, wasting memory and reducing hit rates. The correct key is cache:products:${productId}.