Scheduled AI Research and Reporting
Scheduled AI Research and Reporting
Key jargon
| Term | Plain-language meaning |
|---|---|
| Scheduler | A service that starts work at a configured time or interval. |
| Freshness window | The maximum acceptable age of information for a report. |
| Source ledger | A record of consulted sources, dates, evidence strength, and supported claims. |
| Publication gate | Checks that must pass before a generated artifact is distributed. |
Key concepts
- Separate collection, extraction, synthesis, verification, and publishing into observable stages.
- Scheduled generation is not self-validating; stale sources and silent partial failures must block publication.
Concept map
flowchart LR
A["Schedule bounded collection"] --> B["Extract with source ledger"]
B --> C["Synthesize and verify claims"]
C --> D["Approve then publish"]Controlled cycle
- Fixed query and source policy.
- Date/cutoff and bounded collection budget.
- Deduplicate against prior source IDs.
- Extract claims with source/date/evidence status.
- Reconcile contradictions and staleness.
- Draft changes separately from published truth.
- Human review before publication.
- Record no-change outcome and next scheduled run.
Loop brakes
Maximum sources, pages, cost, elapsed time, recursion depth, and consecutive no-change runs. Stop on access barriers, conflicting high-impact evidence, or source-policy failure.
Exercise
Design a weekly model-release digest using only official provider release notes. Specify change detection and how a retracted or silently edited release is handled.