Ask for research, not a verdict
Prompts such as what should I buy or will this asset rise ask the model to compress uncertainty into an action or prediction. A better prompt defines a research task: identify primary sources, summarize competing mechanisms, list missing data, and propose verification steps. This produces a reviewable workflow rather than a confident answer with hidden assumptions.
State that the output is informational and that unsupported claims should be marked unknown. Ask the model to abstain when evidence is insufficient. An explicit uncertainty requirement does not guarantee honesty, but it makes unsupported certainty easier to detect and gives the system a permitted alternative to inventing an answer.
Define scope, entity, and time
Specify the exact company, instrument, token contract, market, jurisdiction, and period. Similar names and ticker changes can cause entity confusion. Include an as-of timestamp and ask the assistant to identify source dates and effective dates. A request covering current conditions should reject sources older than a stated threshold unless they provide necessary history.
Define the desired horizon and unit. Revenue growth over a fiscal year, intraday price movement, and a regulatory status are different questions. Ask the model to flag incompatible currencies, accounting periods, adjusted data, and percentage bases. Precision in the prompt reduces ambiguity but does not remove the need to verify output.
Require a source hierarchy
Tell the assistant to prefer original filings, regulator publications, exchange notices, official documentation, and direct datasets. Secondary commentary can provide interpretation but should not silently replace the original evidence. Request a link and a short explanation of what each source supports, along with material caveats found nearby.
Prohibit invented citations and ask for unknown when a primary source cannot be located. Then open every source. Models can still fabricate links, cite a real document for the wrong claim, or quote language from a different version. Source requirements improve the audit trail; they are not automatic fact checking.
Request assumptions and counterarguments
Ask the model to separate observed fact, calculation, inference, and scenario. For each conclusion, request assumptions and the evidence that would invalidate it. Then ask for the strongest alternative explanation and for data that could distinguish the alternatives. This structure reduces the chance that one narrative fills every gap.
Use symmetrical language. Instead of list reasons this trade will work, request evidence supporting and contradicting the thesis, including neutral outcomes. Ask which stakeholders benefit from each source or claim. Prompt balance cannot eliminate training bias, but it avoids building the desired answer into the question itself.
Make calculations inspectable
For numerical work, request input values, units, formulas, intermediate steps, rounding, and source dates. Recalculate independently with a calculator or spreadsheet. Models may make arithmetic errors, mix units, or use inconsistent denominators while producing a plausible final number. A correct formula with wrong inputs remains wrong.
For historical tests, define the universe, timestamps, missing data, corporate actions, costs, execution assumptions, and out-of-sample period. Ask the assistant to list look-ahead, survivorship, and selection-bias risks. Generated code should be reviewed and tested; a successful run only proves that the code executed, not that the experiment answered the intended question.
Use staged prompts instead of one giant request
Break the task into source discovery, extraction, calculation, counterargument, and final synthesis. Review the output and evidence between stages. A single prompt that asks for research, prediction, recommendation, and execution hides where an error entered and encourages the model to bridge missing evidence with plausible text.
Give each stage a narrow output schema and explicit stop conditions. If primary sources conflict or a required date is missing, the workflow should pause rather than continue to a verdict. Staging does not guarantee correctness, but it makes omissions, unsupported transitions, and changes in assumptions easier to identify before they propagate.
End every prompt with a verification plan
Ask which claims are time-sensitive, which require a professional, and which sources should be checked manually. Require a concise list of unresolved questions and a confidence explanation tied to evidence rather than a bare percentage. Save the prompt and output in the research journal so changes can be traced.
Tyrian Trade AI Assistant can help structure these questions but cannot make a market decision safe, predict returns, or provide personalized advice. Never include passwords, private keys, confidential account data, or nonpublic personal information in a prompt. Better prompting improves process clarity; it does not turn generated text into authority.