SKILL.md
Prompt Caching
Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation)
Capabilities
- prompt-cache
- response-cache
- kv-cache
- cag-patterns
- cache-invalidation
Prerequisites
- Knowledge: Caching fundamentals, LLM API usage, Hash functions
- Skills_recommended: context-window-management
Scope
- Does_not_cover: CDN caching, Database query caching, Static asset caching
- Boundaries: Focus is LLM-specific caching, Covers prompt and response caching
Ecosystem
Primary_tools
- Anthropic Prompt Caching - Native prompt caching in Claude API
- Redis - In-memory cache for responses
- OpenAI Caching - Automatic caching in OpenAI API
Patterns
Anthropic Prompt Caching
Use Claude's native prompt caching for repeated prefixes
When to use: Using Claude API with stable system prompts or context
import Anthropic from '@anthropic-ai/sdk';
const client = new Anthropic();
// Cache the stable parts of your prompt
async function queryWithCaching(userQuery: string) {
const response = await client.messages.create({
model: "claude-sonnet-4-20250514",
max_tokens: 1024,
system: [
{
type: "text",
text: LONG_SYSTEM_PROMPT, // Your detailed instructions
cache_control: { type: "ephemeral" } // Cache this!
},
{
type: "text",
text: KNOWLEDGE_BASE, // Large static context
cache_control: { type: "ephemeral" }
}
],
messages: [
{ role: "user", content: userQuery } // Dynamic part
]
});
// Check cache usage
console.log(`Cache read: ${response.usage.cache_read_input_tokens}`);
console.log(`Cache write: ${response.usage.cache_creation_input_tokens}`);
return response;}
// Cost savings: 90% reduction on cached tokens
// Latency savings: Up to 2x faster
Response Caching
Cache full LLM responses for identical or similar queries
When to use: Same queries asked repeatedly
import { createHash } from 'crypto';
import Redis from 'ioredis';
const redis = new Redis(process.env.REDIS_URL);
class ResponseCache {
private ttl = 3600; // 1 hour default
// Exact match caching
async getCached(prompt: string): Promise<string | null> {
const key = this.hashPrompt(prompt);
return await redis.get(`response:${key}`);
}
async setCached(prompt: string, response: string): Promise<void> {
const key = this.hashPrompt(prompt);
await redis.set(`response:${key}`, response, 'EX', this.ttl);
}
private hashPrompt(prompt: string): string {
return createHash('sha256').update(prompt).digest('hex');
}
// Semantic similarity caching
async getSemanticallySimilar(
prompt: string,
threshold: number = 0.95
): Promise<string | null> {
const embedding = await embed(prompt);
const similar = await this.vectorCache.search(embedding, 1);
if (similar.length && similar[0].similarity > threshold) {
return await redis.get(`response:${similar[0].id}`);
}
return null;
}
// Temperature-aware caching
async getCachedWithParams(
prompt: string,
params: { temperature: number; model: string }
): Promise<string | null> {
// Only cache low-temperature responses
if (params.temperature > 0.5) return null;
const key = this.hashPrompt(
`${prompt}|${params.model}|${params.temperature}`
);
return await redis.get(`response:${key}`);
}}
Cache Augmented Generation (CAG)
Pre-cache documents in prompt instead of RAG retrieval
When to use: Document corpus is stable and fits in context
// CAG: Pre-compute document context, cache in prompt
// Better than RAG when:
// - Documents are stable
// - Total fits in context window
// - Latency is critical
class CAGSystem {
private cachedContext: string | null = null;
private lastUpdate: number = 0;
async buildCachedContext(documents: Document[]): Promise<void> {
// Pre-process and format documents
const formatted = documents.map(d =>
`## ${d.title}\n${d.content}`
).join('\n\n');
// Store with timestamp
this.cachedContext = formatted;
this.lastUpdate = Date.now();
}
async query(userQuery: string): Promise<string> {
// Use cached context directly in prompt
const response = await client.messages.create({
model: "claude-sonnet-4-20250514",
max_tokens: 1024,
system: [
{
type: "text",
text: "You are a helpful assistant with access to the following documentation.",
cache_control: { type: "ephemeral" }
},
{
type: "text",
text: this.cachedContext!, // Pre-cached docs
cache_control: { type: "ephemeral" }
}
],
messages: [{ role: "user", content: userQuery }]
});
return response.content[0].text;
}
// Periodic refresh
async refreshIfNeeded(documents: Document[]): Promise<void> {
const stale = Date.now() - this.lastUpdate > 3600000; // 1 hour
if (stale) {
await this.buildCachedContext(documents);
}
}}
// CAG vs RAG decision matrix:
// | Factor | CAG Better | RAG Better |
// |------------------|------------|------------|
// | Corpus size | < 100K tokens | > 100K tokens |
// | Update frequency | Low | High |
// | Latency needs | Critical | Flexible |
// | Query specificity| General | Specific |
Sharp Edges
Cache miss causes latency spike with additional overhead
Severity: HIGH
Situation: Slow response when cache miss, slower than no caching
Symptoms:
- Slow responses on cache miss
- Cache hit rate below 50%
- Higher latency than uncached
Why this breaks:
Cache check adds latency.
Cache write adds more latency.
Miss + overhead > no caching.
Recommended fix:
// Optimize for cache misses, not just hits
class OptimizedCache {
async queryWithCache(prompt: string): Promise {
const cacheKey = this.hash(prompt);
// Non-blocking cache check
const cachedPromise = this.cache.get(cacheKey);
const llmPromise = this.queryLLM(prompt);
// Race: use cache if available before LLM returns
const cached = await Promise.race([
cachedPromise,
sleep(50).then(() => null) // 50ms cache timeout
]);
if (cached) {
// Cancel LLM request if possible
return cached;
}
// Cache miss: continue with LLM
const response = await llmPromise;
// Async cache write (don't block response)
this.cache.set(cacheKey, response).catch(console.error);
return response;
}}
// Alternative: Probabilistic caching
// Only cache if query matches known high-frequency patterns
class SelectiveCache {
private patterns: Map<string, number> = new Map();
shouldCache(prompt: string): boolean {
const pattern = this.extractPattern(prompt);
const frequency = this.patterns.get(pattern) || 0;
// Only cache high-frequency patterns
return frequency > 10;
}
recordQuery(prompt: string): void {
const pattern = this.extractPattern(prompt);
this.patterns.set(pattern, (this.patterns.get(pattern) || 0) + 1);
}}
Cached responses become incorrect over time
Severity: HIGH
Situation: Users get outdated or wrong information from cache
Symptoms:
- Users report wrong information
- Answers don't match current data
- Complaints about outdated responses
Why this breaks:
Source data changed.
No cache invalidation.
Long TTLs for dynamic data.
Recommended fix:
// Implement proper cache invalidation
class InvalidatingCache {
// Version-based invalidation
private cacheVersion = 1;
getCacheKey(prompt: string): string {
return `v${this.cacheVersion}:${this.hash(prompt)}`;
}
invalidateAll(): void {
this.cacheVersion++;
// Old keys automatically become orphaned
}
// Content-hash invalidation
async setWithContentHash(
key: string,
response: string,
sourceContent: string
): Promise<void> {
const contentHash = this.hash(sourceContent);
await this.cache.set(key, {
response,
contentHash,
timestamp: Date.now()
});
}
async getIfValid(
key: string,
currentSourceContent: string
): Promise<string | null> {
const cached = await this.cache.get(key);
if (!cached) return null;
// Check if source content changed
const currentHash = this.hash(currentSourceContent);
if (cached.contentHash !== currentHash) {
await this.cache.delete(key);
return null;
}
return cached.response;
}
// Event-based invalidation
onSourceUpdate(sourceId: string): void {
// Invalidate all caches that used this source
this.invalidateByTag(`source:${sourceId}`);
}}
Prompt caching doesn't work due to prefix changes
Severity: MEDIUM
Situation: Cache misses despite similar prompts
Symptoms:
- Cache hit rate lower than expected
- Cache creation tokens high, read low
- Similar prompts not hitting cache
Why this breaks:
Anthropic caching requires exact prefix match.
Timestamps or dynamic content in prefix.
Different message order.
Recommended fix:
// Structure prompts for optimal caching
class CacheOptimizedPrompts {
// WRONG: Dynamic content in cached prefix
buildPromptBad(query: string): SystemMessage[] {
return [
{
type: "text",
text: You are helpful. Current time: ${new Date()}, // BREAKS CACHE!
cache_control: { type: "ephemeral" }
}
];
}
// RIGHT: Static prefix, dynamic at end
buildPromptGood(query: string): SystemMessage[] {
return [
{
type: "text",
text: STATIC_SYSTEM_PROMPT, // Never changes
cache_control: { type: "ephemeral" }
},
{
type: "text",
text: STATIC_KNOWLEDGE_BASE, // Rarely changes
cache_control: { type: "ephemeral" }
}
// Dynamic content goes in messages, NOT system
];
}
// Prefix ordering matters
buildWithConsistentOrder(components: string[]): SystemMessage[] {
// Sort components for consistent ordering
const sorted = [...components].sort();
return sorted.map((c, i) => ({
type: "text",
text: c,
cache_control: i === sorted.length - 1
? { type: "ephemeral" }
: undefined // Only cache the full prefix
}));
}}
Validation Checks
Caching High Temperature Responses
Severity: WARNING
Message: Caching with high temperature. Responses are non-deterministic.
Fix action: Only cache responses with temperature <= 0.5
Cache Without TTL
Severity: WARNING
Message: Cache without TTL. May serve stale data indefinitely.
Fix action: Set appropriate TTL based on data freshness requirements
Dynamic Content in Cached Prefix
Severity: WARNING
Message: Dynamic content in cached prefix. Will cause cache misses.
Fix action: Move dynamic content outside of cache_control blocks
No Cache Metrics
Severity: INFO
Message: Cache without hit/miss tracking. Can't measure effectiveness.
Fix action: Add cache hit/miss metrics and logging
Collaboration
Delegation Triggers
- context window|token -> context-window-management (Need context optimization)
- rag|retrieval -> rag-implementation (Need retrieval system)
- memory -> conversation-memory (Need memory persistence)
High-Performance LLM System
Skills: prompt-caching, context-window-management, rag-implementation
Workflow:
1. Analyze query patterns
2. Implement prompt caching for stable prefixes
3. Add response caching for frequent queries
4. Consider CAG for stable document sets
5. Monitor and optimize hit ratesRelated Skills
Works well with: context-window-management, rag-implementation, conversation-memory
When to Use
- User mentions or implies: prompt caching
- User mentions or implies: cache prompt
- User mentions or implies: response cache
- User mentions or implies: cag
- User mentions or implies: cache augmented
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.