[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.] [@ksaksham39](/creator/twitter/ksaksham39) "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"  [@ksaksham39](/creator/x/ksaksham39) on [X](/post/tweet/1947664651438690790) 2025-07-22 14:26:53 UTC XXX followers, XXX engagements "Day 1: ObservabilityWhy Most LLM Apps Fail in Production Im learning production LLM engineering in XX days. Follow to learn alongside meone topic no jargon all practical. Lets start with why demos die after launch: observability. Most teams only check if their app is up. Thats not enough for LLMs. Production failures happen for reasons you cant spot with simple uptime checks. What real observability means for LLMs: You want to see not just if your app is livebut if your model is giving useful safe and accurate answers. Its working is different from answers make sense and cost isnt exploding"  [@ksaksham39](/creator/x/ksaksham39) on [X](/post/tweet/1947305536614973745) 2025-07-21 14:39:53 UTC XXX followers, XXX engagements
[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.]
@ksaksham39
"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"
@ksaksham39 on X 2025-07-22 14:26:53 UTC XXX followers, XXX engagements
"Day 1: ObservabilityWhy Most LLM Apps Fail in Production Im learning production LLM engineering in XX days. Follow to learn alongside meone topic no jargon all practical. Lets start with why demos die after launch: observability. Most teams only check if their app is up. Thats not enough for LLMs. Production failures happen for reasons you cant spot with simple uptime checks. What real observability means for LLMs: You want to see not just if your app is livebut if your model is giving useful safe and accurate answers. Its working is different from answers make sense and cost isnt exploding" @ksaksham39 on X 2025-07-21 14:39:53 UTC XXX followers, XXX engagements