[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: 2025-07-21 14:39:53 UTC Day 1: Observability—Why Most LLM Apps Fail in Production I’m learning production LLM engineering in XX days. Follow to learn alongside me—one topic, no jargon, all practical. Let’s start with why demos die after launch: observability. Most teams only check if their app is up. That’s not enough for LLMs. Production failures happen for reasons you can’t spot with simple uptime checks. What real observability means for LLMs: You want to see not just if your app is live—but if your model is giving useful, safe, and accurate answers. “It’s working” is different from “answers make sense” and “cost isn’t exploding overnight.” Three pillars to track from Day 1: Every request’s speed: Check both runtime and how many tokens each answer uses (too many = burning cash). Output quality: Don’t just check for errors—set up a simple score or feedback for how good and factual each LLM answer actually is. Spend in real time: Know your cost per question, and watch for spikes or drifts over time. Why most devs miss this: Traditional monitoring is about system errors. LLMs break in subtler ways, like suddenly giving unreliable answers or drifting from your expected topic. Tag every request with basic info: what was asked, what docs were used, model’s answer, and a quality rating. This lets you pinpoint: was it a bad doc, a model glitch, or a strange prompt? Debugging gets 10× faster. Production teams focus on the “why” behind every answer—not just “did I get an answer?” Takeaway: LLM apps need more than uptime—track quality, speed, and cost from day one. That’s observability in action. Tomorrow: How smart caching can slash your OpenAI bill in half. Stick around. Each day, we go deeper—so you can build LLM systems that work when it matters. XXX engagements  **Related Topics** [llm](/topic/llm) [Post Link](https://x.com/ksaksham39/status/1947305536614973745)
[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: 2025-07-21 14:39:53 UTC
Day 1: Observability—Why Most LLM Apps Fail in Production
I’m learning production LLM engineering in XX days. Follow to learn alongside me—one topic, no jargon, all practical.
Let’s start with why demos die after launch: observability.
Most teams only check if their app is up. That’s not enough for LLMs. Production failures happen for reasons you can’t spot with simple uptime checks.
What real observability means for LLMs:
You want to see not just if your app is live—but if your model is giving useful, safe, and accurate answers.
“It’s working” is different from “answers make sense” and “cost isn’t exploding overnight.”
Three pillars to track from Day 1:
Every request’s speed: Check both runtime and how many tokens each answer uses (too many = burning cash).
Output quality: Don’t just check for errors—set up a simple score or feedback for how good and factual each LLM answer actually is.
Spend in real time: Know your cost per question, and watch for spikes or drifts over time.
Why most devs miss this: Traditional monitoring is about system errors. LLMs break in subtler ways, like suddenly giving unreliable answers or drifting from your expected topic.
Tag every request with basic info: what was asked, what docs were used, model’s answer, and a quality rating.
This lets you pinpoint: was it a bad doc, a model glitch, or a strange prompt? Debugging gets 10× faster.
Production teams focus on the “why” behind every answer—not just “did I get an answer?”
Takeaway: LLM apps need more than uptime—track quality, speed, and cost from day one. That’s observability in action.
Tomorrow: How smart caching can slash your OpenAI bill in half.
Stick around. Each day, we go deeper—so you can build LLM systems that work when it matters.
XXX engagements
Related Topics llm