The Missing Layer in Your AI Stack: Intermediate Knowledge

Every AI product starts the same way:

Call an LLM
Return the output
Ship fast

And it works… until scale hits. Costs balloon, latency drags, and your answers start to wobble. At that point, most teams scramble to add caching — but caching final responses only gets you so far.

What You Actually Need

What you actually need is an Intermediate Knowledge Layer (IKL): a thin, versioned cache that sits between your data sources and your LLM, storing the ingredients (retrieval results, API responses, summaries) that feed your prompts.

Think of IKL as your data warehouse for prompts:

Structured, reusable, and timestamped artifacts
Different TTLs for different sources (e.g. API = 5 minutes, docs = 24 hours)
Cheap invalidation when only one piece of context changes

This gives you speed, consistency, and precision without duct-taping your prompt layer.

The Architecture

Instead of this traditional flow:

Data Sources → LLM → Response

You get this optimized flow:

Data Sources → IKL → LLM → Response
                ↑
            (cached ingredients)

Benefits

Speed: Skip expensive retrieval and API calls when ingredients are fresh
Consistency: Same inputs always produce the same context
Cost Control: Reduce redundant data fetching and processing
Debugging: Inspect exactly what context fed into each prompt
Versioning: Track how your knowledge evolves over time

Implementation Example

Here's a lightweight implementation using FastAPI and Redis:

from fastapi import FastAPI
from redis import Redis
import json
import hashlib
from datetime import datetime, timedelta
from typing import Dict, Any, Optional

app = FastAPI()
redis_client = Redis(host='localhost', port=6379, db=0)

class IntermediateKnowledgeLayer:
    def __init__(self, redis_client: Redis):
        self.redis = redis_client
    
    def _generate_key(self, source: str, query: str) -> str:
        """Generate a unique key for the knowledge artifact"""
        content = f"{source}:{query}"
        return f"ikl:{hashlib.md5(content.encode()).hexdigest()}"
    
    def store_knowledge(
        self, 
        source: str, 
        query: str, 
        data: Dict[Any, Any], 
        ttl_seconds: int = 3600
    ) -> str:
        """Store knowledge artifact with TTL"""
        key = self._generate_key(source, query)
        
        artifact = {
            "source": source,
            "query": query,
            "data": data,
            "timestamp": datetime.utcnow().isoformat(),
            "ttl": ttl_seconds
        }
        
        self.redis.setex(key, ttl_seconds, json.dumps(artifact))
        return key
    
    def get_knowledge(self, source: str, query: str) -> Optional[Dict[Any, Any]]:
        """Retrieve knowledge artifact if it exists and is fresh"""
        key = self._generate_key(source, query)
        cached = self.redis.get(key)
        
        if cached:
            return json.loads(cached)
        return None
    
    def invalidate_source(self, source: str):
        """Invalidate all artifacts from a specific source"""
        pattern = f"ikl:*"
        for key in self.redis.scan_iter(match=pattern):
            artifact = json.loads(self.redis.get(key))
            if artifact.get("source") == source:
                self.redis.delete(key)

# Usage example
ikl = IntermediateKnowledgeLayer(redis_client)

@app.post("/query")
async def process_query(query: str):
    # Check for cached API data
    api_data = ikl.get_knowledge("external_api", query)
    if not api_data:
        # Fetch from API and cache for 5 minutes
        fresh_data = await fetch_from_external_api(query)
        ikl.store_knowledge("external_api", query, fresh_data, ttl_seconds=300)
        api_data = fresh_data
    
    # Check for cached document retrieval
    doc_data = ikl.get_knowledge("documents", query)
    if not doc_data:
        # Retrieve documents and cache for 24 hours
        fresh_docs = await retrieve_documents(query)
        ikl.store_knowledge("documents", query, fresh_docs, ttl_seconds=86400)
        doc_data = fresh_docs
    
    # Combine cached ingredients and send to LLM
    context = combine_context(api_data, doc_data)
    response = await call_llm_with_context(query, context)
    
    return {"response": response, "cached_sources": ["external_api", "documents"]}

In production, you’ll want richer cache keys than this simple demo. Include things like model version, temperature bucket, user scope, and a context version tag so you get high hit rates without mixing up results.

Key Patterns

Source-Specific TTLs: Different data sources need different refresh rates
Granular Invalidation: Update only what changed, not everything
Artifact Versioning: Track how your knowledge base evolves
Context Composition: Mix and match cached ingredients efficiently

When to Use IKL

You need an Intermediate Knowledge Layer when:

Your AI app makes repeated calls to the same data sources
Context preparation is expensive (API calls, document retrieval, processing)
You need consistent responses for the same inputs
Debugging prompt context is becoming difficult
Costs are scaling faster than usage

The Bottom Line

Most AI scaling problems aren't LLM problems—they're data problems. An Intermediate Knowledge Layer gives you the control and efficiency you need to build AI products that work at scale.

Stop caching responses. Start caching knowledge.