On this page
Learning objectives
- Design a repeatable evidence collection pipeline
- Separate observed facts, customer language, and analyst inference
- Create a research brief with citations and confidence notes
Prerequisites
A research question tied to a real campaign or product decision
Answer first
Canonical definition
An AI research workflow collects approved sources, extracts evidence into a consistent schema, and produces an analyst-reviewed synthesis whose claims remain traceable to their origin.
Operating context
Why it matters
The useful output is not a long summary. It is a decision-ready view of evidence, disagreement, gaps, and implications.
Source discipline makes the workflow reusable by content, sales, product, and leadership teams.
Method
Step by step
- 01
Frame the decision
Write the decision this research will support, the audience, the deadline, and what would change your mind.
- 02
Set source policy
Define allowed source types, recency, geography, exclusions, and how first-party customer evidence is handled.
- 03
Extract before synthesizing
Capture quotes, facts, dates, URLs, entities, and confidence separately before asking a model to identify themes.
- 04
Review the brief
A human checks pivotal claims, missing counter-evidence, and whether recommendations exceed the evidence.
Hands-on lab
Build a cited market brief
Collect ten sources around one buying question and normalize them into a source ledger before synthesis.
Deliverable
A two-page brief with evidence table, competing interpretations, open questions, and recommended next research action.
What breaks
Common failure modes
- F1Letting model-generated summaries erase the source trail
- F2Mixing customer quotes with analyst paraphrases
- F3Treating search rank or repetition as proof of accuracy
Beyond the demo
Production notes
- Store source snapshots or access dates for material claims because pages change.
- Use a fixed evidence schema so briefs can be compared and retrieved later.
Further reading
Sources
Related Tenten resources