75% LLM Cost Reduction: The 7-Layer Optimization Pipeline
How Knol's extraction pipeline uses prompt caching, batching, model routing, and deduplication to cut LLM costs by 75% without sacrificing quality.
The Seven Layers
Knol's cost optimization isn't a single technique — it's a orchestrated pipeline of seven complementary strategies:
1. Semantic Deduplication
When multiple sources convey the same fact, why extract it multiple times? Knol hashes semantic content to identify duplicates before sending to the LLM.
Conversation 1: "I live in San Francisco"
Conversation 2: "My city is SF"
→ Deduplicated to one extraction request**Savings**: 15-20% on extraction volume
2. Prompt Caching with API Providers
OpenAI, Anthropic, and others offer prompt caching. System prompts and extraction instructions don't change between calls — they should be cached. Knol automatically batches extractions to maximize cache hits.
**Savings**: 25% on token costs (50% cheaper for cached tokens)
3. Intent-Based Model Routing
Not all extraction tasks need GPT-4. Simple fact extraction from recent conversations routes to Claude 3.5 Haiku. Complex disambiguation routes to Claude Opus. Routing decisions happen in real-time based on query complexity.
**Savings**: 40% overall by using the right model for each task
4. Batch Processing
Instead of extracting facts one-at-a-time, Knol batches 50-100 conversation turns per API call. This amortizes overhead and enables dynamic model routing based on batch characteristics.
**Savings**: 10-15% through batching efficiency
5. Working Memory Bypass
For queries in the current session, bypass extraction entirely. The working memory layer contains fresh data that doesn't need semantic analysis.
**Savings**: 30% of retrieval queries never hit the LLM
6. Conflict Resolution Caching
When Knol detects conflicting facts, it caches resolution decisions. "User prefers Postgres over MySQL" doesn't get re-extracted every time a new database preference is mentioned.
**Savings**: 5-10% for repeat patterns
7. Cross-Tenant Extraction Pooling
In multi-tenant deployments, Knol pools similar extraction tasks across customers and deduplicates at the semantic level. This requires privacy-preserving anonymization but can save 10-20% in shared deployments.
**Combined Savings**: 75% on total LLM invocation costs
Real-World Impact
A customer with 100,000 monthly conversations: - Baseline extraction cost: $2,500/month - With 7-layer optimization: $625/month - Annual savings: $22,500 per customer
And the extracted memories are actually *better*, because deduplication, conflict detection, and temporal modeling create higher-quality semantic data.