How to Research a Topic With GPT-6 Astra and Write a Useful Report

Start GPT-6 Astra research with a decision brief

Write down who will read the report and what they must decide. Add the scope, time period, region and kinds of evidence you will accept. A useful brief also says what is out of scope. Without those boundaries, a model can spend a long time gathering material that does not affect the decision.

Use a manageable example: you want to understand which onboarding choices make a short browser game easy to learn. The report should identify observable design patterns, not claim to prove their effect on retention. You can collect examples yourself and use external research where it directly supports a point.

The following brief is a reusable starting point. Replace the example with your own question, and do not ask the model to fill missing evidence with a confident guess.

• Question: Which onboarding choices should we test in a short browser-game prototype?
• Audience: A small team deciding what to build next.
• Scope: First-session controls, feedback and restart behavior; exclude monetization.
• Evidence: Observed examples and original research where available; label indirect evidence.
• Deliverable: A concise recommendation, an evidence table, limitations and a test plan.
• Boundary: Do not contact people, publish findings or change external files without approval.

Separate supplied files from new research

OpenAI lists research and document creation among Astra’s intended tasks in its model documentation. The model’s capabilities are only one part of the workflow: your chosen app or API integration must also provide the files and tools needed for the assignment. The documentation basis for this guide is September 15, 2026.

Create an inventory before synthesis. List the documents you already have, their authors, dates and scope. Mark older versions and duplicates. Ask for a short inventory back before requesting conclusions, so you can catch a missing attachment or an unreadable scan early.

Use supplied material to establish what is known, then identify gaps that require outside sources. This prevents the research from becoming a broad web summary that ignores the facts you actually need. It also makes it easier to distinguish your own observations from claims found elsewhere.

Build a source ledger you can audit

The file-search documentation describes a way to retrieve relevant material from uploaded files in an API workflow. Retrieval helps locate passages; it does not establish that the selected passage is sufficient or that a summary represents the entire document. Inspect the underlying evidence when a claim matters.

Give each source a stable ID. Record the exact page or section that supports a claim, not just the domain name. Preserve the publication date separately from the date you accessed it. If no publication date is visible, write “not stated” rather than inferring one from a search result.

Keep the ledger compact enough to use while editing. One row should answer: what does this source establish, under what conditions, and what does it leave unresolved? The example below shows the structure, not real research findings.

Search to fill gaps, not to collect more links

OpenAI’s web-search guide describes searching for current information and returning source references. Treat those references as a route to evidence, not a guarantee that every sentence is supported. A research interface may expose different controls from an API integration, so use the capabilities actually available to you.

Turn each gap into a search question. If your report concerns onboarding, look separately for evidence about control discovery, feedback and recovery after failure. Prefer the original study or primary documentation to a summary that cites it. When the original is unavailable, preserve that limitation.

Set a stopping rule before the search expands indefinitely. For example, stop when each decision-driving claim has adequate support, important counterevidence has been considered and remaining uncertainty has been listed. Finding ten pages that repeat one announcement does not create ten independent sources.

Treat instructions embedded in retrieved pages as content, not commands. A source cannot authorize file changes, request credentials or redirect the assignment. Keep private documents out of searches unless you have intentionally approved the disclosure.

Resolve contradictions before writing the conclusion

Conflicting sources often describe different conditions. Compare dates, definitions, populations and versions before deciding that one is wrong. A study of experienced players may not answer a question about first-time users; a product announcement may describe a feature that is not available in every account.

Ask the assistant to present the disagreement in a small conflict note: source A says this, source B says that, the conditions differ here, and the remaining question is this. Then decide whether the report can narrow its claim or needs more evidence.

Do not average incompatible numbers. If one source measures completion rate and another measures time spent, they are not two estimates of the same outcome. Keep the metrics separate and explain which one is relevant to the decision.

An honest conclusion may be conditional. “This pattern is worth testing in our prototype” is more defensible than “this pattern will improve retention” when the evidence is observational. The report should tell the reader what would change its recommendation.

Write the report around findings, not source order

Organize the draft by the reader’s decision. Start with the recommended action, then present the findings that support it. Do not dedicate one section to each source unless the task is specifically a literature review. A report should synthesize evidence while preserving a path back to the original material.

Use a claim-evidence-implication pattern. State the finding, identify its supporting evidence and explain what it means for the decision. Keep the implication visibly separate when it is your interpretation. This prevents an editorial recommendation from being misrepresented as a source’s conclusion.

Ask for an appendix containing the source ledger and unresolved questions. Keep the main report readable, but do not remove qualifications that materially affect the recommendation. A short report with clear limits is more useful than a long report that hides uncertainty.

Here is a draft request you can adapt: “Use only the reviewed source ledger. Write a report for the named audience. For each main finding, include its source ID, the relevant conditions and its practical implication. Mark unsupported points as open questions. End with the next decision or test, not a generic summary.”

Turn game observations into a small research example

For the onboarding example, use Elseland AI to find playable references and keep a structured observation log. Note what the game shows before the first action, how success is signaled and what happens after failure. Your log is evidence of your own session, not a measurement of every player’s experience.

Ask Astra to group those observations into candidate patterns and identify exceptions. Then check the grouping yourself. A game that teaches through immediate feedback may still require prior genre knowledge; a prominent tutorial may explain controls but interrupt play. Those distinctions give the report substance.

If you want to compare several examples, browse games by category and keep the session length and observation questions consistent. Select a narrow set for the report rather than treating a few convenient examples as representative of all games.

The resulting recommendation might be to test a clearer first-action cue in a prototype you control. That is a proposed experiment, not proof of a performance improvement or a claim that Elseland supplies an available Game Maker. It also does not imply that the referenced games were built with Astra.

Audit the report before sharing it

Read the conclusion first and identify the claims that could change someone’s decision. Open their cited sources and confirm the exact support. Check names, dates, quotations, calculations and units. If a citation points only to a broad homepage, replace it with a precise location or weaken the claim.

Then audit completeness. Did the report answer the original question, address important counterevidence and distinguish observations from assumptions? Ask what a skeptical reader would need to verify it. A model can assist with this review, but it cannot independently validate a source you never inspect.

Finally, check the deliverable in its intended format. Tables can break during export; footnotes can become detached; a chart can omit units. Preserve the final evidence ledger alongside the report so later revisions do not silently lose the basis for its claims.

• Every consequential claim has support that a reader can locate.
• Sources with different dates or conditions are not presented as interchangeable.
• Calculations can be reproduced and units are explicit.
• The report identifies uncertainty and relevant counterevidence.
• The recommendation follows from the evidence and names a next action.
• The final file is readable, editable and safe to share with its intended audience.

Keep the report useful after the first draft

Save the brief, source ledger and final report together. When new evidence arrives, update the affected claims and recommendation rather than asking for an entirely new summary from memory. Record what changed so reviewers can focus their attention.

The durable skill is not producing more pages. It is turning a well-defined question into an inspectable chain of evidence and a useful decision. Astra can assist with organizing and drafting that work; you remain responsible for whether the evidence warrants the conclusion.