Building Production Ready AI Applications with Claude APIs.
A demo takes an afternoon. Production takes the things nobody demos: retries, evals, cost control, and a plan for the day the model changes.
9 min read
- Demo to production
- 4-6 weeks
- Unclear
- Cost per request
- Tracked per feature
- Unmeasured
- Regression detection
- Eval suite
- User reports
The first version always works.
You wire up the API, write a prompt, and the output looks great.
Then a user sends something you did not anticipate, and there is no fallback.
Then the bill arrives and nobody can say which feature caused it.
Production is not a better prompt. It is the scaffolding around the prompt.
§01 · The problem
Where the time actually goes.
- 01No evaluation suite before shipping
- 02Prompts stored in code with no versioning
- 03No timeout, retry, or fallback path
- 04Streaming bolted on after launch
- 05Cost untracked at the feature level
- 06No plan for prompt drift when models update
Every one of these is invisible in a demo and unavoidable in production.
§02 · The mechanism
The AI workflow.
- 01
Define the task and success criteria
Scoping - 02
Build the eval set before the feature
Setup - 03
Wire the API with retries and timeouts
Development - 04
Add guardrails and a fallback path
Development - 05
Instrument cost and latency per call
Automation - 06
Run evals on every prompt change
Validation
A person closes the loop: the workflow ends on a human or a check against a defined outcome.
- Scoping
- A decision about what is in and out
- Setup
- One-time configuration, done before the loop runs
- Development
- Engineering work inside the workflow
- Automation
- Deterministic. No model involved
- Validation
- Checking the result against a defined outcome
§03 · The difference
Before vs after.
| Before | After |
|---|---|
| Prompt written directly in the handler | Prompts versioned like any other artifact |
| Quality judged by eyeballing outputs | Eval suite scores every change |
| One failure mode takes down the feature | Retries, timeouts, and a graceful fallback |
| Cost discovered on the monthly invoice | Cost tracked per feature and per user |
| Model update silently changes behaviour | Model updates gated behind evals |
| Rollback means reverting a deploy | Rollback is a config change |
§04 · The measure
Time saved.
| Task | Before | With AI |
|---|---|---|
| Prompt change confidence | Hope | Eval score |
| Failure handling | None | Retry and fallback |
| Cost attribution | Whole account | Per feature |
| Demo to production | Unclear | 4-6 weeks |
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