A API Development Prompts

API Response Caching Strategy

Design a multi-layer API response caching strategy with cache invalidation, conditional requests, cache warming, and CDN integration.

Fill in the placeholders

Edit the values, then copy your finished prompt.

[framework]

[rps]

[endpoint_count]

[cache_backend]

[cache_key_format]

[min_ttl]

[max_ttl]

[model_type]

[event_bus]

[cdn_provider]

[warm_count]

[target_hit_ratio]

[alert_threshold]

Your Prompt

prompt.txt

You are an API performance engineer. Design a comprehensive caching strategy for a Laravel API that handles 5,000 requests per second with 50 endpoints.

Step 1: Audit all 50 API endpoints and classify each by cacheability: fully cacheable (static reference data, public listings), conditionally cacheable (user-specific data that changes infrequently), and non-cacheable (real-time data, write operations). For each cacheable endpoint, determine the appropriate TTL based on data freshness requirements. Document the classification in a table: endpoint | cache type | TTL | invalidation trigger.

Step 2: Implement application-level caching using Redis. For each cacheable endpoint, generate a cache key from the request path, query parameters, and relevant headers (Accept-Language, Authorization for user-scoped caches). Store the serialized response with its Content-Type and status code. Use api:{version}:{path}:{query_hash} as the key format. Set per-endpoint TTLs ranging from 60 seconds for frequently changing data to 24 hours for reference data.

Step 3: Implement HTTP conditional request support. Add ETag headers (content hash) and Last-Modified headers to all cacheable responses. Handle If-None-Match and If-Modified-Since request headers by checking the cache before executing the database query. Return 304 Not Modified when the content has not changed, saving bandwidth and processing. Implement weak ETags for list endpoints where minor changes should not invalidate the cache.

Step 4: Design the cache invalidation strategy. Implement event-driven invalidation: when a Product model is created, updated, or deleted, publish an invalidation event to Redis Pub/Sub. The cache subscriber processes the event and purges all affected cache keys using tag-based invalidation. Map each model type to the set of cache tags it affects. Handle cascade invalidation where updating a parent record invalidates child listing caches.

Step 5: Set up CDN-level caching for public endpoints using CloudFront. Configure Cache-Control headers with appropriate max-age and s-maxage directives. Implement Surrogate-Key headers for targeted CDN purging. Set up a cache warming job that pre-fetches the top 100 most-requested endpoints after a deployment or cache purge.

Step 6: Build a cache monitoring dashboard that tracks: hit ratio per endpoint (target 90%), average response time for cache hits vs misses, cache memory usage, invalidation frequency, and stale-while-revalidate effectiveness. Alert when the hit ratio drops below 70% for any high-traffic endpoint.

What this prompt does

This prompt makes the AI an API performance engineer designing a multi-layer caching strategy for a [framework] API handling [rps] requests per second across [endpoint_count] endpoints. It audits and classifies every endpoint by cacheability, implements application-level caching in [cache_backend], adds HTTP conditional requests, designs event-driven invalidation, sets up CDN caching via [cdn_provider], and builds a monitoring dashboard. Cache keys combine path, query, and relevant headers, with TTLs from [min_ttl] to [max_ttl].

The structure works because aggressive caching is only safe when invalidation is reliable. The event-driven, tag-based invalidation step is the linchpin: when a [model_type] model is created, updated, or deleted, it publishes to [event_bus], and the subscriber purges every affected cache key by tag, including cascade invalidation where updating a parent record clears the child listing caches it appears in. ETag and Last-Modified support lets the API answer with 304 Not Modified and skip the database query entirely, saving bandwidth and compute, while CDN s-maxage plus Surrogate-Key headers extend caching to the edge with targeted purges instead of full flushes. Classifying every endpoint first ensures user-scoped data never lands in a shared cache.

When to use it

A hot API needs caching layered from the app through to the CDN
You want to classify endpoints by cacheability before caching anything
You need event-driven invalidation so caching doesn't serve stale data
You want conditional requests returning 304 to cut bandwidth
You're extending caching to the edge with targeted CDN purges
You want a dashboard tracking hit ratio against [target_hit_ratio]
You're caching user-scoped responses and must avoid leaking data between users

Example output

Expect a layered strategy: an endpoint-classification table with cache type, TTL, and invalidation trigger per endpoint across [endpoint_count] endpoints, application caching in [cache_backend] with [cache_key_format] keys built from path, query, and relevant headers and TTLs from [min_ttl] to [max_ttl], ETag and Last-Modified handling for 304 responses, an event-driven tag-based invalidation design keyed off [model_type] changes through [event_bus], CDN configuration with Cache-Control, s-maxage, and Surrogate-Key purging, plus a cache-warming job and a monitoring dashboard tracking hit ratio against [target_hit_ratio] with alerts below [alert_threshold]. It's structured as labeled [framework] steps.

Pro tips

Classify all [endpoint_count] endpoints first; caching the wrong endpoint serves stale or user-leaked data
Make invalidation event-driven and tag-based — it's what makes caching at [min_ttl]–[max_ttl] TTLs safe to ship
Include user-scoping headers like Authorization in keys for user-specific caches, or you'll leak data between users
Add ETags so the API can return 304 and skip the database query entirely on unchanged content
Use s-maxage for the CDN and Surrogate-Key headers so you can purge targeted content at the edge instead of flushing everything
Handle cascade invalidation: updating a parent record must also purge the child listing caches it appears in

Frequently Asked Questions

How does the caching avoid serving stale data?

It uses event-driven, tag-based invalidation: when a `[model_type]` model is created, updated, or deleted, an event is published to `[event_bus]` and a subscriber purges every cache key tagged with that model, including cascade invalidation of related child listings. That's what makes aggressive TTLs between `[min_ttl]` and `[max_ttl]` safe.

What do ETags and conditional requests add on top of caching?

ETag and Last-Modified headers let clients send `If-None-Match` or `If-Modified-Since`, and the API returns 304 Not Modified when content is unchanged. That skips the database query and avoids resending the body, saving bandwidth and processing on top of the application and CDN cache layers.

Can I cache user-specific endpoints without leaking data between users?

Yes, but the cache key must include user-scoping headers like `Authorization` so each user gets a distinct entry. The prompt classifies such endpoints as conditionally cacheable with shorter TTLs, and the key generation incorporates the relevant headers to keep one user's response out of another's cache.

How does CDN caching fit with the application cache?

The application layer caches in `[cache_backend]` while the CDN caches public endpoints at the edge using `Cache-Control` with `s-maxage`. Surrogate-Key headers allow targeted CDN purges, and a cache-warming job pre-fetches top endpoints after a deploy or purge, so the two layers complement rather than duplicate each other.

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Engr Mejba Ahmed

AI Developer · Software Engineer

I'm Mejba — I design and ship production AI systems, automations, and full-stack apps. If you want this turned into a working solution for your team, let's talk.

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Category: API Development Prompts
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Published: Mar 2026

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API Response Caching Strategy

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Engr Mejba Ahmed

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