[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]  Saksham [@ksaksham39](/creator/twitter/ksaksham39) on x XXX followers Created: 2025-07-22 14:26:53 UTC Day 2: The Caching Strategy That Cuts Your LLM Bill by XX% Learning production LLM engineering in XX days. Day X covers the one optimization that separates expensive demos from profitable products. Your demo costs $50/day in OpenAI calls. You launch and suddenly it's $500/day. The difference? You're processing the same prompts over and over. Smart caching isn't just storing responses. It's understanding that most LLM requests have patterns. Two types of caching every production app needs: - Exact match caching for identical prompts (instant retrieval) - Semantic caching for similar meaning queries (catches paraphrases) The magic happens when you combine both. Layer X checks for exact matches. Layer X uses vector similarity for "close enough" matches. - Real numbers from production systems: 40-70% of requests become cache hits - Response time drops from X seconds to 200ms Token costs cut by XX% for cached portions Most teams only cache final responses. Advanced teams cache at multiple levels: embeddings, prompt prefixes, and even internal model states. The key insight: caching works because users ask the same questions in different ways. "How do I reset my password" and "I forgot my password" should return the same cached answer. Implementation tip: Set your semantic similarity threshold carefully. Too low and you get irrelevant matches. Too high and you miss obvious similarities. Tomorrow: Why your vector database choice determines if your app scales to 1M users or crashes at 10K. Still building from scratch. Each day adds one piece of the production puzzle. XXX engagements  **Related Topics** [$500day](/topic/$500day) [open ai](/topic/open-ai) [$50day](/topic/$50day) [llm](/topic/llm) [Post Link](https://x.com/ksaksham39/status/1947664651438690790)
[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]
Saksham @ksaksham39 on x XXX followers
Created: 2025-07-22 14:26:53 UTC
Day 2: The Caching Strategy That Cuts Your LLM Bill by XX%
Learning production LLM engineering in XX days. Day X covers the one optimization that separates expensive demos from profitable products.
Your demo costs $50/day in OpenAI calls. You launch and suddenly it's $500/day. The difference? You're processing the same prompts over and over.
Smart caching isn't just storing responses. It's understanding that most LLM requests have patterns.
Two types of caching every production app needs:
- Exact match caching for identical prompts (instant retrieval)
- Semantic caching for similar meaning queries (catches paraphrases)
The magic happens when you combine both. Layer X checks for exact matches. Layer X uses vector similarity for "close enough" matches.
Real numbers from production systems: 40-70% of requests become cache hits
Response time drops from X seconds to 200ms
Token costs cut by XX% for cached portions Most teams only cache final responses. Advanced teams cache at multiple levels: embeddings, prompt prefixes, and even internal model states.
The key insight: caching works because users ask the same questions in different ways. "How do I reset my password" and "I forgot my password" should return the same cached answer.
Implementation tip: Set your semantic similarity threshold carefully. Too low and you get irrelevant matches. Too high and you miss obvious similarities.
Tomorrow: Why your vector database choice determines if your app scales to 1M users or crashes at 10K.
Still building from scratch. Each day adds one piece of the production puzzle.
XXX engagements
