2.3.2 - Why Cutting Features Is a Design Skill in AI-Assisted Software Development

Feature cut

Are you cutting features that distract from the main user path?

The call

Cut before AI adds more. Otherwise every option AI generates becomes a feature and the product grows without getting better.

Why it matters

Cutting features is a design skill because every removal decision reshapes how users move through the product. AI can suggest many options quickly, but human judgement decides which cuts improve clarity and which create risk. The difference is between simpler software that works and smaller software that confuses users.

Explainer

A feature cut is not an omission by accident. It is a design decision that protects the core path from noise. Until you can name one core path, one feature to cut and one reason the cut strengthens the product, the product will keep expanding. AI can help add surface area fast, but it cannot decide what should stay out.

Make the feature cut concrete

Compare the broad version with a version you can actually test.

• Too vague: We should probably keep the search tool simple.
• Concrete enough to test: We are cutting query history from version one because the core test is whether a content creator can search with their saved context and act on a result in one session. History adds UI complexity without proving the context layer works.

The second version lets two people remove the same complexity from it.

Check the feature cut

• Pass: You can say what stays central, what gets removed and why the product becomes stronger because of that cut.
• Fail: If the cut still sounds like we will keep it simple for now, it is not concrete enough yet.

Do not move into implementation or polish work until this passes.

How to use AI for the feature cut

• AI chat: Rewrite the feature cut until you can state all three parts clearly.
• vibeCoding: Build the thinnest flow that tests this feature cut in practice before broader build work.
• AI-assisted coding: Carry the same feature cut into implementation and review so the live system keeps the same decision.

Sharpen the feature cut

Copy this prompt into AI chat, replace the bracketed lines with your real feature cut and keep the instruction exactly as visible here.

You are checking whether this feature cut is clear enough before you move forward.

Constraint:
The feature cut must be specific enough that two people would remove the same complexity from it.

Working draft:
Core path to protect: [what must stay central]
Feature to cut: [what gets removed]
Why the cut strengthens it: [why the product gets stronger because of that cut]

Task:
Decide whether this feature cut is specific enough to guide the next decision. If it is vague, rewrite it so two people would make the same decision from this feature cut.

Check:
- Would two people interpret this the same way?
- Does it stay concrete enough to guide the next step?
- Does it meet this bar: You can say what stays central, what gets removed and why the product becomes stronger because of that cut.

Return:
- A corrected feature cut
- A short explanation of what was vague

Evaluation

Before accepting the result, check whether two people would remove the same complexity from it.

Example

To help you work through this, here is a real example. StartWithYourContext is an AI search tool built as part of the vibe2value project. Here is how its feature cut was written using the three parts:

• Core path to protect: A content creator searches with their saved context and acts on a result in one session.
• Feature to cut: Query history. Users cannot see or revisit past searches in version one.
• Why the cut strengthens it: History adds UI complexity and storage overhead without proving the core question: does context make results better? Cutting it keeps the test clean.

That feature cut is specific enough that two people would remove the same complexity from it.

When there is more than one side

Not every product has a single set of features to cut. When a system serves more than one side, a feature that is noise for one side may be essential for the other.

Multi-sided worked example

For example, StartWithYourContext has two different feature cut perspectives:

• Content creator: Cut query history and multiple contexts. Keep the single-context search flow. The core test is whether context improves results, not whether users revisit old queries.
• Developer: Cut configuration options and pluggable providers. Keep the fixed stack. The core test is whether a developer can understand the integration by reading it, not whether they can swap parts out.

Both cuts protect focus, but they protect different things. If only one side’s cuts are made, the other side’s noise stays in.

Risk and mitigation

• Risk: Cutting a feature that looked optional but protected a critical edge case, which can degrade trust after release.
• Mitigation: Define one user-critical scenario before each cut and validate that scenario with real usage signals before rollout.

Key takeaway

Do not move forward until you can say what stays central, what gets removed and why the product becomes stronger because of that cut.

Work through this in a workshop

If your feature cuts are still unclear, bring them to a free weekly workshop. Bring the messy part of your AI-assisted build and leave with a clearer next step. In some sessions, we walk through practical examples on the Cloudflare Workers stack to show how a rough idea turns into something that actually runs.

What do you think?

How are you deciding which features to cut in AI-assisted software development and how is AI helping you make those design calls with confidence?