Hold My BEER: 4 Steps To Make Any Feature Production Ready With AI
Stop building AI features like a madman. I'll show you the structured way.
Hey there,
Building AI features shouldn't feel like wrestling a greased pig.
Too often, I see developers jump into building complex AI-powered features with the idea of achieving a production-ready masterpiece right out of the gate. This shotgun approach with AI usually leads to overwhelming prompts, context overload, and ultimately, subpar results. It's a recipe for frustration and inefficiency, and frankly, it's not how we build great software.
We work iteratively, and AI development demands the same structured, incremental approach.
That's why I want to introduce you to the BEER framework, a simple yet incredibly effective way to leverage AI for building application features.
Stop wasting time on bloated, ineffective AI prompts.
Build features that truly delight your users.
Iterate and improve your AI integrations with confidence.
This framework is your roadmap to sanity and success in AI development.
4 Steps To Build AI Features With Contextual Understanding— Even If You're New to AI
In order to achieve polished, robust AI-driven features, you're going to need a handful of things: a systematic approach and a willingness to iterate. The BEER framework provides exactly that structure.
Let's break down how you can take any feature idea and build it out, layer by layer, using AI.
B: Build
This is where you lay the foundational groundwork.
Think of this as the absolute minimum, the MVP of your feature. Let's say you want to build a reminder application. The 'Build' phase means creating the core functionality: the ability to create a reminder. This requires the basic structure – a title, a date, and perhaps a 'done' flag. You don't need fancy UI or complex logic yet. Just get the fundamental piece working. I often start by asking AI for a basic data model and the core CRUD operations for this.
It’s about proving the concept and getting something tangible.
E: Enhance
Once you have your bare-bones feature, it's time to make it better from a user experience perspective.
This is where the 'Enhance' step comes in. For our reminder app, this means adding essential validation. Think: ensuring the title isn't empty, or that the reminder date is in the future. These are usability improvements that make the feature more robust and less prone to errors. They refine the interaction and prevent common user mistakes.
This step is crucial for making your AI-generated functionality feel like a polished part of your application.
E: Expand
Now that you have a functional and enhanced feature, it's time to 'Expand' upon it.
This isn't about adding a completely new, unrelated feature. Instead, you're adding more capabilities within the scope of the existing feature. For our reminder app, expanding could mean adding the ability to edit existing reminders, delete them, or even set recurring reminders. A critical expansion I always push for is adding notification capabilities – ensuring users are actually prompted when their reminder is due.
This makes the feature truly valuable and comprehensive.
R: Refine
This is the final polish.
The 'Refine' stage is all about user interface (UI) and user experience (UX) improvements to make the feature production-ready. This could involve making the UI more visually appealing, ensuring button consistency, optimizing for mobile responsiveness, creating custom icons, or crafting clearer error messages. It's about taking a working, functional feature and making it seamless and professional.
You want it to look and feel like it belongs, not like an afterthought an AI cobbled together.
10 Hours → 30 Minutes With AI
One of the hardest parts of shipping side-project features is turning a fuzzy idea into tiny, testable coding tasks you can actually hand to an AI or a teammate. You know what the feature should do — but not the exact atomic steps that make it easy to build, verify, and iterate without breaking unrelated code.
This prompt solves that by enforcing the BEER rhythm (Build → Enhance → Expand → Refine) and an exact, repeatable output format: one-line goal, copy-paste code-generator instructions, and acceptance tests per step. What makes it effective is its discipline — every step is atomic, includes data models and API contracts, and limits scope to one feature so you don’t accidentally refactor the whole app.
Why this matters for side projects: small teams and solo devs win by shipping minimal, testable increments. Explicit, machine-readable tasks let coding AIs produce predictable results and let you validate each change with concrete curl/behavior checks.
How to use it:
Run the prompt — it will ask you to provide FEATURE_NAME, PRIMARY_USER_SCENARIO, and optional STACK_PREFERENCE and PROJECT_TYPE.
AI will split your feature into the 4 BEER steps.
Execute & test — give one step at a time to your code generator, run the listed acceptance tests, and commit when green.
Ready to convert a feature idea into executable tasks? Paste your INPUT (FEATURE_NAME, PRIMARY_USER_SCENARIO, STACK_PREFERENCE, PROJECT_TYPE) and run the prompt below.
You are an expert Product Engineer + AI Architect.
Your role: Generate **clear, structured, implementation instructions** for an AI coding tool (e.g., Lovable, Bolt) to follow.
Each step of the BEER framework (Build, Enhance, Expand, Refine) should return **precise coding tasks** that can be executed one at a time by a code generator.
The scope of the changes is only one feature: don't modify unrelated existing modules.
---
## INPUT I will give you:
- FEATURE_NAME: [FEATURE_NAME]
- PRIMARY_USER_SCENARIO: [PRIMARY_USER_SCENARIO]
- STACK_PREFERENCE: [optional, e.g. "TypeScript + Fastify + Prisma" or "React + Node + MongoDB"]
- PROJECT_TYPE: [optional, e.g. "fullstack web application (Nuxt, Next, SvelteKit or similar)", "pure frontend SPA (React, Vue or similar with no backend)", "REST of GraphQL API + frontend")
---
## OUTPUT STRUCTURE
For each BEER step, return instructions in **this exact format**:
### Step: [Build / Enhance / Expand / Refine]
1. **Goal (one line)** — what this step will achieve in the app.
2. **Code Generator Instructions** — clear, copy-paste task list for a coding AI, written as imperative steps.
Example format:
- Define a data model [with fields]. // Optional
- Create an API endpoint [method + path]. // If the project includes a backend
- Implement [core logic].
- Add validation rules: [list].
- Return sample requests/responses.
3. **Acceptance Tests (bullet list)** — scenarios or curl commands to validate the implementation.
---
## GLOBAL RULES
- Each step must be **atomic** (can be built & tested independently).
- No step should include more than one new feature at a time.
- Always show data models + API contracts explicitly.
- Use simple, declarative language so a coding tool can parse it easily.
- Assume the AI coding tool has memory of previous steps. Only include new/changed instructions per step.
---
## EXAMPLE (Reminder App)
### Step: Build
1. **Goal:** Enable users to create a reminder with a title and due date.
2. **Code Generator Instructions:**
- Create a `Reminder` model with fields: `id (uuid)`, `title (string, required)`, `dueDate (datetime, required)`, `isDone (boolean, default=false)`.
- Create API endpoint: `POST /reminders`.
- Implement logic: store reminder in database.
- Return JSON: `{ id, title, dueDate, isDone }`.
3. **Acceptance Tests:**
- ✅ Create a reminder with title + due date → returns reminder JSON.
- ❌ Create with missing title → returns 400.
- ❌ Create with past dueDate → returns 400.
---
## FINAL DELIVERY
- Present all 4 BEER steps in order (Build → Enhance → Expand → Refine).
- Each step formatted exactly as above.
- End with a **1-line TL;DR** summarizing the feature journey.
---
Start by asking me to provide the INPUT and give me examples of each field.
Once I provide the INPUT begin with Step: Build.Here's what you learned today:
The BEER framework (Build, Enhance, Expand, Refine) provides a structured approach to AI feature development.
Iterative development is key to managing AI's complexity and ensuring user adoption.
Focus on one user scenario at a time, applying BEER to each for maximum impact.
Remember, the goal is to build AI features that are not just functional, but also intuitive and valuable for your users.
Don't try to do it all at once.
Hit reply and let us know why.
Now go build!
See ya next week,
— Ale & Manuel
loved this one :) the temptation with AI is always to build something complex and impressive, but starting with the absolute MVF makes so much sense. Build the core, then layer on the intelligence.