← Home

Most AI Writing Feels Generic. Here’s the 5-Step Method I Use to Make It Useful

Feb 26, 2026

TL;DR

AI made writing fast, but it also made generic writing cheap.

If your post sounds fluent but doesn’t change what a reader does next, it’s not useful yet. My fix is a 5-step method: start from a real problem, include one concrete example, show trade-offs, separate facts from inference, and end with an implementation checklist.

The result is slower drafting, but much better signal.

Context

A year ago, the bottleneck was producing a coherent first draft. Now that bottleneck is mostly gone.

Today, almost anyone can generate readable prose in minutes. That means readability is no longer the edge. The new edge is whether your writing helps readers make better decisions.

This is where many AI-assisted posts fail: they optimize for polish over substance. They summarize what everyone already knows and avoid the hard part—specific claims, trade-offs, and operational guidance.

Key Points

1) Start from a real problem, not a broad topic

Weak start: “Write about AI content strategy.”

Strong start: “My AI-assisted posts get views, but few saves/replies. How do I make them decision-useful?”

Problem-first framing forces relevance. Topic-first framing often produces generic summaries.

2) Anchor one concrete example from your workflow

Specificity creates trust.

Even one small, honest example can carry an entire post:

Then rewrite around one failure and one actionable checklist. The post becomes more memorable and more reusable.

3) Show one trade-off and one failure mode

Advice without constraints is usually noise.

If you recommend “be more specific,” show the cost: specificity takes longer. Also show failure modes:

Trade-offs make the writing credible.

4) Separate facts, inference, and opinion

Use a simple labeling discipline:

This avoids “authoritative tone, weak evidence” — a common AI-writing failure pattern.

5) End with a checklist readers can run today

A good post should convert into action quickly.

If readers cannot apply your advice in 5–10 minutes, it’s likely too abstract.

Steps / Code

Use this template to force specificity in AI-assisted drafts:

You are helping me write one high-signal blog post.

Topic:
[insert topic]

Audience:
[insert audience]

Core problem:
[insert real decision/problem the reader faces]

Non-obvious thesis (one sentence):
[insert specific, arguable claim]

Constraints:
- Include one real example from my workflow.
- Include one trade-off and one failure mode.
- Distinguish fact vs inference.
- Use plain language; remove buzzword filler.
- End with a 5-item implementation checklist.

Output format:
1) TL;DR
2) Context
3) Key Points
4) Steps / Code
5) Trade-offs
6) References
7) Final Take
8) Changelog

Implementation checklist for each post:

  1. Define the reader’s decision in one sentence.
  2. Add one concrete example (numbers/timeline if possible).
  3. Add one trade-off and one failure mode.
  4. Verify each factual claim has a source.
  5. End with clear next actions.

Trade-offs

This method is not optimized for raw speed.

Costs

Benefits

If your goal is durable writing (not just daily output), this trade is usually worth it.

References

Final Take

In 2026, fluent writing is abundant.

The moat is not speed; it’s whether your writing contains specific, testable, decision-useful thinking. AI can produce words instantly, but only you can provide grounded judgment.

Use AI for draft velocity, then earn trust with specificity.

Changelog