All Playbooks
AI Integrationintermediate

Shipping AI Features Without the Hype Tax

Most AI features ship as a demo that survives one round of investor questions and then quietly dies in production. This is the discipline that gets AI features past that wall: small scope, real evals, careful rollouts, and instrumentation that catches drift early. The same loop I run when I add AI capabilities to an existing product, on a real timeline with real users.

120 min7 steps
7

Steps

5

Tools

5

Outcomes

intermediate

Difficulty

Technologies used

AnthropicOpenAIVercel AI SDKPostgreSQLNext.js

The methodology

The phases, in order

Each phase below is something I actually run in a project. The descriptions are how I think about the work, not abstract definitions.

01

Phase

Phase 1 of 7

Scope a Feature That Can Win

I pick a feature with a measurable win condition: time saved, conversion lift, support tickets deflected. Anything vaguer than that gets refused. Then I write the prompt that would solve the smallest viable version, before any UI, to confirm the model can actually do the task. Pair with my AI integration service.
02

Phase

Phase 2 of 7

Pick the Right Model

Model choice is workload-dependent, not vibe-dependent. I compare candidates on accuracy on my eval set, latency, cost per request, and rate limit ceiling. The cheapest model that hits the quality bar wins. See OpenAI vs Anthropic for a head-to-head on the major providers.
03

Phase

Phase 3 of 7

Build a Prompt Library

Prompts live in code, versioned and reviewed like any other change. I separate system prompts from user-facing copy so designers can iterate on the latter without touching model behavior. Each prompt has a unit test that checks for must-have phrases and must-avoid phrases in a fixed set of representative inputs.
04

Phase

Phase 4 of 7

Add Evaluations Before Shipping

I build an eval set of 50 to 200 representative inputs with expected outcomes. The eval harness runs on every prompt change, model change, or pipeline change, and reports the delta in a CI comment. Without evals, every change is a vibe check, and vibe checks lie at scale.
05

Phase

Phase 5 of 7

Ship Behind a Feature Flag

First rollout is 1 percent of users, then 10, then 50, then 100. Each step requires the previous step to look healthy on latency, cost, error rate, and the business metric the feature was supposed to move. The kill switch is one toggle, tested before the rollout starts.
06

Phase

Phase 6 of 7

Instrument Latency, Cost, and Quality

Every AI request logs model, prompt version, tokens in, tokens out, latency, cost, and a quality signal where available. I build a dashboard that shows these per feature, so cost regressions and quality regressions are visible the same day they happen. Integrates with the monitoring playbook.
07

Phase

Phase 7 of 7

Iterate on Real Data

After a week in production I look at the worst 50 interactions and the best 50, by user feedback or by automatic quality score. The pattern in those tails is what drives the next prompt change. This loop, repeated weekly, is what turns a fragile demo into a feature that earns its place in the product.

Results

What You'll Achieve

Expected outcomes from implementing this playbook

AI features that survive contact with real users
Cost and latency under control with visible budgets
Clear evaluation metrics on every change
A safe rollback path the on-call team trusts
Start a project if you want a partner who has shipped this before.

Use this playbook

Want me to run this with you?

The playbook is the public version. The private version is me running it for your team against a real deadline. If you have a project on the line, that is usually the faster path.

Start a projectJust ask a question

Next up

Architecture · 180 min

Migrating a Monolith to Microservices

Most monolith-to-microservices stories end as cautionary tales because the team tried to design the future architecture instead of evolving toward it. This playbook is the staged migration I run: map the domain, find natural seams, extract behind a stable façade, adopt event-driven communication where it pays off, and decommission the old system gradually. Boring, slow, and the only version that consistently works.

AI Integration

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Production Monitoring & Observability

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Migrating a Monolith to Microservices