Vibe coding and product creation Assemble manually
From prompt to production: what to add to the AI feature
A successful prompt is still not production-ready. In the CodeVibers episode, we break down the engineering layers needed between a demo prompt and an AI feature ready for users: input/output contracts, eval dataset, failure taxonomy, runtime guardrails, observability, cost limits, and rollback.
Core of the topic
A successful prompt is still not production-ready. In the CodeVibers episode, we break down the engineering layers needed between a demo prompt and an AI feature ready for users: input/output contracts, eval dataset, failure taxonomy, runtime guardrails, observability, cost limits, and rollback.
What’s important to take
- After viewing, the viewer can take one working prompt and describe the minimal production contour before deployment.
- Production checklist for AI feature: contract (input/output), test dataset, evals, guardrails, observability, cost limits, and rollback.
- This release isn’t about magic prompt engineering or model selection—it’s about the engineering layer around prompts: how to make results verifiable, observable, and rollback-capable.
- Improve retention and retention by using a domain-specific L3 checklist instead of a general discussion about AI benefits.
How to apply in practice
Use the material as a starting point: define the task, scope the application area, select a quality metric, and validate the result on a small-scale scenario before production deployment.
Recommendations
- First, fix the entry and exit contracts, not endlessly refine the prompt wording.
- Assemble a small eval dataset from real and synthetic edge cases before the first release.
- Split guardrails into input validation, output validation, tool permissions, and runtime fallback.
Requirements and Limitations
- Each AI feature must have an owner; failure taxonomy: which errors are acceptable, which block responses, and which require rollback.
- Each production call must log: prompt version, model, parameters, retrieved context, tool calls, latency, cost, and final status.
- Before public release, there must be quality thresholds: pass rate on the eval set, p95 latency, cost per task, and maximum manual review rate.
Examples
- AI feature for parsing applications: input—text and attachments; output—structured card; guardrail—cannot create a deal without required fields.
- AI feature for support responses: output is checked for a knowledge base source; if confidence is low, a draft is created for the operator.
Anti-examples
- The prompt worked well on three demo examples, but in production there are no logs, no eval set, no rollback, and no one knows why the response quality declined after switching models.
How to check readiness
- The eval set must include at least 30 cases: happy path, dirty input, missing context, adversarial prompt, expensive tool call, and ambiguous request.
- For release, 4 metrics are tracked: pass rate, p95 latency, cost per successful task, and manual review rate.
Release Navigation
- 00:00 Splash screen
- 00:06 Who is CodeVibers and what is the topic?
- 00:49 Why demo prompt ≠ production
- 01:08 Entry and Exit Contract
- 01:27 Evals and runtime guardrails
- 02:30 Failure taxonomy
- 02:51 Expert Quality Cycle
- 04:10 Runtime guardrails
- 05:14 Observability, Cost, and Tracing
- 05:34 Release gate before production