Claude Code for LLM Caching Workflow Tutorial

LLM caching is one of the most impactful optimizations you can add to your AI-powered applications. By storing and reusing responses for identical or similar requests, you can dramatically reduce API costs, decrease response latency, and handle higher traffic volumes without hitting rate limits. In this tutorial, you’ll learn how to build a robust LLM caching workflow using Claude Code and Claude Skills.

Why LLM Caching Matters

Every time you send a prompt to an LLM, you’re paying for the full computation—even if you’ve asked the exact same question before. For applications with repetitive queries, user FAQs, or structured data extraction tasks, caching can reduce costs by 50-90% while improving response times from seconds to milliseconds.

The challenge is that LLMs don’t have built-in caching like traditional databases. You need to implement it yourself, and that’s where Claude Code skills become invaluable.

Setting Up Your Caching Infrastructure

Before building the caching workflow, you need to decide on your storage backend. For most use cases, Redis offers the best balance of speed, persistence, and simplicity. Let’s create a skill that handles cache operations.

Redis-Based Cache Skill

Create a new skill file llm-cache-skill.md in your skills directory:

---
name: llm-cache
description: "Cache and retrieve LLM responses with intelligent key generation"
---

# LLM Response Cache Skill

This skill manages caching of LLM responses using Redis with smart key generation.

## Cache Key Generation

Generate deterministic cache keys from prompts:

```python
import hashlib
import json

def generate_cache_key(prompt: str, model: str, temperature: float) -> str:
    """Create a unique, deterministic cache key."""
    payload = {
        "prompt": prompt.strip(),
        "model": model,
        "temperature": temperature
    }
    payload_str = json.dumps(payload, sort_keys=True)
    hash_obj = hashlib.sha256(payload_str.encode())
    return f"llm:cache:{model}:{hash_obj.hexdigest()[:16]}"

Cache Retrieval

Check if a cached response exists before calling the LLM:

async def get_cached_response(key: str, redis_client) -> str | None:
    """Retrieve cached response if available."""
    cached = await redis_client.get(key)
    if cached:
        # Log cache hit for metrics
        print(f"Cache HIT for key: {key[:20]}...")
        return cached.decode('utf-8')
    print(f"Cache MISS for key: {key[:20]}...")
    return None

Cache Storage

Store responses with optional TTL (time-to-live):

async def cache_response(key: str, response: str, ttl: int = 3600):
    """Store LLM response with configurable expiration."""
    await redis_client.setex(key, ttl, response)
    print(f"Cached response with TTL: {ttl}s")

Implementing the Cached LLM Workflow

Now let’s build the complete workflow that ties everything together. This is where Claude Code shines—you can create a skill that orchestrates the entire process.

Complete Caching Workflow

class LLMCacheWorkflow:
    def __init__(self, redis_client, llm_client):
        self.redis = redis_client
        self.llm = llm_client
    
    async def execute(self, prompt: str, **llm_params):
        # Step 1: Generate cache key
        cache_key = generate_cache_key(prompt, 
                                       llm_params.get('model', 'claude-3-5-sonnet'),
                                       llm_params.get('temperature', 0.7))
        
        # Step 2: Check cache
        cached = await get_cached_response(cache_key, self.redis)
        if cached:
            return {
                "response": cached,
                "cached": True,
                "cache_key": cache_key
            }
        
        # Step 3: Call LLM if not cached
        llm_response = await self.llm.complete(prompt, **llm_params)
        
        # Step 4: Store in cache
        await cache_response(cache_key, llm_response)
        
        return {
            "response": llm_response,
            "cached": False,
            "cache_key": cache_key
        }

Advanced Caching Strategies

Basic exact-match caching is powerful, but you can take it further with semantic caching. This approach caches responses for prompts that are “similar enough” to previous ones.

Semantic Caching with Embeddings

from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

class SemanticCache:
    def __init__(self, redis_client, embedding_model, similarity_threshold=0.95):
        self.redis = redis_client
        self.embedder = embedding_model
        self.threshold = similarity_threshold
    
    async def get_or_compute(self, prompt: str, compute_fn):
        # Generate embedding for current prompt
        current_embedding = await self.embedder.embed(prompt)
        
        # Search for similar cached prompts
        cached_prompts = await self.redis.zrange("semantic:prompts", 0, -1)
        
        for cached_key in cached_prompts:
            cached_embedding = await self.redis.get(f"embed:{cached_key}")
            similarity = cosine_similarity([current_embedding], 
                                          [cached_embedding])[0][0]
            
            if similarity >= self.threshold:
                # Return cached response
                response = await self.redis.get(f"response:{cached_key}")
                return response, True
        
        # No match found - compute fresh response
        response = await compute_fn(prompt)
        
        # Store in semantic cache
        cache_key = generate_cache_key(prompt, "semantic", 0)
        await self.redis.set(f"response:{cache_key}", response)
        await self.redis.zadd("semantic:prompts", {cache_key: time.time()})
        
        return response, False

Handling Cache Invalidation

Caching introduces the challenge of stale data. You need a solid invalidation strategy.

Invalidation Patterns

class CacheInvalidator:
    def __init__(self, redis_client):
        self.redis = redis_client
    
    async def invalidate_by_pattern(self, pattern: str):
        """Invalidate all keys matching a pattern."""
        keys = await self.redis.keys(pattern)
        if keys:
            await self.redis.delete(*keys)
            print(f"Invalidated {len(keys)} cache entries")
    
    async def invalidate_by_tag(self, tag: str):
        """Invalidate all cache entries with a specific tag."""
        await self.invalidate_by_pattern(f"llm:cache:*:{tag}:*")
    
    async def invalidate_model(self, model: str):
        """Clear all cache for a specific model."""
        await self.invalidate_by_pattern(f"llm:cache:{model}:*")

Best Practices for Production

When deploying LLM caching in production, follow these guidelines:

Set appropriate TTL values: FAQ-style content can cache for days, while dynamic queries should have shorter TTLs (minutes to hours).
Monitor cache hit rates: A healthy production cache should achieve 60-80% hit rates for typical applications.
Implement cache warming: Pre-populate cache with frequently requested prompts during startup for better user experience.
Handle cache failures gracefully: Never let cache errors break your application—fall back to direct LLM calls.
Use structured prompts: Consistent prompt formatting improves cache effectiveness by increasing exact-match hits.

Testing Your Caching Implementation

Finally, verify your implementation works correctly:

import pytest

async def test_cache_workflow():
    redis = await create_test_redis()
    llm = MockLLMClient()
    workflow = LLMCacheWorkflow(redis, llm)
    
    # First call - cache miss
    result1 = await workflow.execute("What is Python?")
    assert result1["cached"] == False
    
    # Second call - cache hit
    result2 = await workflow.execute("What is Python?")
    assert result2["cached"] == True
    assert result1["response"] == result2["response"]

Conclusion

Implementing LLM caching with Claude Code is straightforward and delivers immediate benefits. Start with exact-match caching for quick wins, then evolve to semantic caching as your application matures. The skills you create for caching become reusable infrastructure that improves every AI-powered feature in your application.

Remember to monitor your metrics, tune your TTL values, and always have fallback mechanisms in place. With proper implementation, you can significantly reduce costs while delivering faster responses to your users.

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