Implementing Caching with Claude Code
Claude can implement caching correctly, but needs specific requirements. Here's what to include and what to verify before shipping.
Caching is one of those features that looks simple but has enough sharp edges that Claude Code needs careful prompting to get right. The core implementation is easy — the tricky parts are cache invalidation, stale data handling, and failure modes.
Specify the requirements explicitly
A good caching prompt includes:
- What data to cache and for how long
- What the cache key is
- What to do on cache miss
- What to do when the cache backend is unavailable
- Whether to use read-through or write-through caching
- Any invalidation triggers
"Add caching to this function" without this context produces a basic implementation that probably works for the happy path. Edge cases are where it falls apart.
The failure mode Claude often misses
Cache backend unavailability. If Redis goes down, what should happen? Claude's default is often to throw an error — which means a Redis outage takes down your entire application. For most use cases, the correct behavior is to fall through to the source: if the cache is unavailable, query the database directly and continue.
Specify this explicitly: "If the cache backend is unavailable, fall through to the primary data source. Cache failures should not be fatal."
Stale data and invalidation
TTL-based expiry is the simplest approach and Claude implements it correctly. Event-based invalidation — invalidating cached entries when underlying data changes — is harder and requires knowing your data model. Claude can implement it but needs to know the events: "Invalidate the user profile cache when the user updates their name or email."
Race conditions on cache population
The thundering herd problem: if a popular cache entry expires and many concurrent requests all miss and try to populate it simultaneously, you can overload your primary data source. This is easy to miss and Claude won't add protection for it unless you ask. For high-traffic paths, ask Claude to implement a distributed lock or probabilistic early recomputation.
Testing caching is harder than testing most code
Ask Claude to write tests that verify: cache hit returns cached value, cache miss falls through to source, expired entries are refreshed, and cache backend failure is handled gracefully. These edge cases are where caching implementations fail in production.