Choose a real decision
Start with a decision such as which onboarding problem to investigate next. Official OpenAI guidance describes Astra as suited to complex work across code, apps, and research. Availability varies by account and client. The model is a tool for examining the decision; it does not create missing customer evidence.
Build a compact context pack
Provide the goal, target segment, current behavior, relevant source excerpts, constraints, and options already considered. Keep facts separate from assumptions. Include dates for time-sensitive evidence. Use approved workspaces and only information you are authorized to share.
Ask for the strongest counterargument
Instead of asking whether your plan is good, ask what would make it fail. Request alternative explanations, contradictory evidence, and unanswered questions. Tell the model not to invent research or metrics. Ask it to cite supplied source IDs so you can check its interpretation.
Compare alternatives explicitly
Ask for a decision table showing potential value, uncertainty, effort, and evidence needed for each option. Review the inputs instead of accepting a tidy ranking. Estimates and scoring frameworks make assumptions visible; they do not turn uncertain inputs into objective facts.
Make your own recommendation
Verify important claims against their sources, discuss the tradeoffs with relevant colleagues, and write the final recommendation in your own words. Record what evidence would change your mind. Use a lighter tool or setting for routine tasks when it meets the quality bar. Compare on the same inputs rather than buying every new tool.
Source / further reading: Official OpenAI model guidance ↗