Speed matters in publishing, but “fast” is useless if the piece can’t be defended. AI can compress hours of scanning into minutes, yet it also makes it easy to amplify weak sources or merge unrelated claims. The solution is to treat AI as a research assistant with strict output requirements: clusters, citations, and clear uncertainty.
This guide introduces a repeatable research workflow that works well for newsroom-like briefings, indie maker updates, and AI trend posts.
Start with clusters, not paragraphs
Writing too early is the most common mistake. First build a story map: what happened, who did it, and why it matters.
What to cluster by
Use grouping signals that match how people search:
- Entity: company, product, person, studio, government body
- Action: launched, acquired, updated policy, shipped feature, banned, partnered
- Metric: price, users, revenue, latency, model size, market share
- Location: market-specific constraints (US/EU/JP/CN)
A small rule that prevents duplication
If two headlines share the same entity and action, they belong in the same cluster unless the time window differs.
Summaries with receipts (citations required)
Your AI prompt should require links, not vibes. If the model can’t cite anything, it should say so.
Return:
1) 5 bullet summary with citations (URLs)
2) "What we do NOT know yet" section
3) confidence score per claim (high/medium/low)
Constraints:
- do not invent numbers
- quote exact phrasing when unsure“No link, no claim” is a simple policy that keeps your archive trustworthy.
Build a research table before you draft
Tables force clarity. They also make it easier to hand off work to a teammate.
| Cluster | Core question | Must-have sources | Angle for readers |
|---|---|---|---|
| Product launch | What changed and for whom? | official release notes, docs | practical impact |
| Market move | Why now? | earnings call, credible reporting | strategy |
| Policy / regulation | What is allowed? | primary policy text | compliance |
| Community signal | Is this real traction? | repo activity, forum posts | early warning |
Example: a finished mini-map
- What happened: concise headline and one-sentence summary.
- Why it matters: one paragraph tying it to the reader’s goals.
- What to do next: checklist or next steps.
Drafting: humans choose the arc, AI fills support
Once the clusters are clean, drafting becomes easy:
- Editors decide the structure: H2 sections, H3 “why it matters,” and a short glossary.
- AI can propose supporting paragraphs, but you keep the final voice consistent.
- Add “owned” examples: your own screenshots, configs, benchmarks, or quotes.
A practical markdown outline
## The headline### The evidence### The implication## What to watch next## Sources
Fact-check and update loops
Treat each article as a living page. When new facts arrive, update the post and add a note.
- Before publish: verify every number, and convert vague words (“massive,” “huge”) into specifics.
- After publish: if a claim changes, append an “Update” note with date and source.
QA checklist (high signal)
- Every paragraph maps to one cluster.
- Claims link to primary or reputable secondary sources.
- Uncertainty is explicitly labeled.
- Headings are descriptive (they become SEO anchors and the outline UI).
- Tags are accurate and specific (
research,workflow,ai).
Closing thought
AI makes scanning faster. Clustering makes thinking clearer. Together they let you publish at speed without sacrificing credibility—and credibility is the asset that compounds in search.