Feature-Chunked AI App Building
Build and verify one functional slice at a time to keep AI coding agents aligned.
- Difficulty
- Easy
- Time to result
- ~days to results
- Steps
- 6
- Confidence
- 99%
Feature-Chunked AI App Building decomposes an application into a sequence of functional slices rather than relying on a single oversized prompt. First define the application's core outcome, then separate the workflow into input, processing, output, and conversion features. Ask the coding assistant to implement one slice, test it with realistic data, and resolve problems before introducing the next dependency. The completed slices are then exercised as one end-to-end workflow. This approach reduces instruction overload, makes failures easier to locate, and preserves a working application throughout the build. Even when an assistant appears capable of one-shotting a complex product, chunking provides a repeatable control mechanism that improves reliability and makes later changes easier to specify.
Origin
Extracted from Marketing Against The Grain during a live walkthrough of building a newsletter lead-generation app with Google's AI coding assistant.
Core principles
- 01Reduce ambiguity before asking an assistant to generate code.
- 02Build features in dependency order.
- 03Verify each functional slice before adding the next.
- 04Prefer controlled iteration even when one-shot generation is possible.
- 05Separate input, transformation, and output capabilities.
How to run it
- 1
Define the Core Outcome
Write one sentence describing what the user supplies and what the application returns. Exclude secondary features from this initial contract.
Pro tip Use an explicit input-to-output statement.
Watch out A vague outcome makes every later feature boundary unstable.
- 2
Decompose the Workflow
Divide the application into independently testable capabilities such as accepting input, parsing it, generating an artifact, downloading it, and capturing contact details.
Pro tip Treat each visible user action or data transformation as a candidate slice.
Watch out Do not divide features so finely that no slice can be tested meaningfully.
- 3
Order by Dependency
Build the capability that later features depend on first. Continue outward from the core transformation toward formatting, downloads, and growth features.
Pro tip Start with the smallest path that proves the central promise.
Watch out Building polish or lead capture before validating the core output can waste effort.
- 4
Implement One Slice
Give the assistant the requirements and acceptance conditions for only the current feature. Preserve the already working behavior while adding it.
Pro tip Include a realistic example of the expected input and output.
Watch out Avoid slipping unrelated changes into the same prompt.
- 5
Test Before Expanding
Exercise the new slice with realistic data and inspect its output. Fix misunderstandings while the change remains localized.
Pro tip Keep a repeatable test case for every completed slice.
Watch out Do not interpret generated code as evidence that the feature works.
- 6
Integrate and Recheck
After all slices exist, run the complete user journey from initial input to final result or conversion. Confirm that later additions did not break earlier behavior.
Pro tip Test both the successful journey and common missing-input cases.
Watch out Individually working features can still fail at their boundaries.
In the wild
The host first builds the ability to upload or paste a job description and identify potential skills. The next feature converts a selected idea into a properly formatted Claude Skill. The final feature lets the user download the resulting file, after which an email-capture overlay is added.
→ Each stage produces testable functionality while progressively completing the lead-generation application.
A consultant first builds a form that accepts customer inputs and calculates a recommendation. Once the calculation is verified, the assistant adds a downloadable report. A final slice adds an email form before export and connects it to the mailing platform.
→ The core calculation is validated before distribution and conversion complexity is introduced.
Common mistakes
Requesting Everything at Once
A giant prompt increases the chance that requirements will be omitted or implemented inconsistently. Divide the workflow even if the model claims it can build the full application in one attempt.
Adding Features Before Testing
Untested errors become harder to isolate after several dependent capabilities have been layered together. Exercise each slice before continuing.
Building in the Wrong Order
Downloads, forms, and visual polish depend on a working core transformation. Implementing them first can create a convincing shell around an invalid result.
Is it for you?
Best for
It is best for people building prototypes or small applications through conversational AI coding tools.
Not ideal for
It is not ideal as a substitute for deliberate architecture and engineering controls in complex or safety-critical systems.
From the transcript
“The other thing is when you're creating an app, it is best to chunk it into parts so the assistant does not get too confused.”
“But to make sure this worked, I first of all built the functionality to upload a job or paste a description into this panel and…”
“And then I build the next feature, which was converting those into actual Clawed skills in the right format, which I'm going to bring you…”
From the episode
I Used Gemini Code Assist to Build a Newsletter App (for Free)