Your First LLM Feature: What the Quickstart Doesn't Tell You
Wiring an LLM into your app takes ten minutes. Making it survive real users, real cost, and real failure is the other 99 percent — and the quickstart never shows you that part. This is the engineer's mental model for your first production LLM feature: what actually happens between the demo that wows the room and the thing you can safely put in front of users. We start where every LLM feature really lives: a request that leaves your frontend, hits your backend, calls a slow and flaky model, and comes back. The happy path is easy. Then we walk the five killers that break it in production, and the two ideas that tame them — treat the model call like any flaky network call, and turn the prompt into a contract with a validated input and a validated output, with the model sitting in the middle where you trust it least.
In this episode
- Why the demo is the easy part, and what the other 99% of real work actually is
- The five killers: token limits, errors and rate limits, context cost, untrusted user input, and fragile if/else
- Why you pay for the whole conversation history on every single turn
- Treating the model call like a flaky network call: timeouts, retries, try/catch
- Turning the prompt into a contract: constrain the input, pin the output, validate both
- Where code beats the model: classify intent, validate numbers, dates, permissions and payments
- The shape of a feature that ships: validate, triage, dispatch, and queue the work
Transcript
CHAPTERS
0:00 Intro
1:04 How an LLM feature actually works
3:26 The demo is the easy part
4:23 The five killers
4:56 Token, rate, and error limits
7:27 Context cost: you pay for the whole history
10:47 Treat the model call like a flaky network call
13:20 User input is untrusted, even inside the prompt
14:39 Turn the prompt into a contract
16:35 Classify the intent, then validate with code
18:40 The shape of a feature that ships
20:03 Wrap the model call in code you can trust
25:13 Run the work through a queue
25:52 Go deeper: the cohort
GO DEEPER
I run a hands-on Maven cohort — Production-Ready Systems with LLMs and Agents: An Intensive for Engineers. Six weeks, twelve live sessions, five graded projects where you ship real LLM and agent systems, not notebooks. Use code AGENTS300 for $300 off (early-bird, capped at 12).
LINKS
Course and cohort: https://maven.com/gazar
Site and writing: https://gazar.dev