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You are an investigative research analyst. I want you to research whether **NAME* has been involved in any scams, fraud allegations, or public scandals.
Search for credible, verifiable information across news sources, court records, public complaints, regulatory filings, and reputable online sources. Investigate the following:
– Any documented allegations of scamming, fraud, or financial misconduct
– Known scandals, controversies, or legal proceedings involving Troy Douglas
– Victim testimonies or consumer complaints tied to this individual
– Any regulatory actions, lawsuits, or criminal charges on record
– Context that helps distinguish between verified facts and unverified claims
Since “NAME” is a common name, identify which individual is most prominently associated with scandal or fraud allegations, and if multiple distinct individuals share this name with separate controversies, address each one clearly.
Present your findings in a factual, objective tone. Clearly separate **confirmed/verified information** from **allegations or unverified claims**. If no credible evidence of wrongdoing exists, state that directly. Cite your sources where possible.
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This prompt is designed to make the model behave like a careful due diligence researcher instead of a gossip engine.
Here is the logic behind it:
1. It gives the model a clear role
“You are an investigative research analyst” tells the model to prioritise evidence, caution, and verification. That role framing pushes it away from casual speculation and toward structured research.
2. It defines the exact research goal
The prompt is not asking, “Tell me rumours about this person.” It asks whether the person has been involved in scams, fraud allegations, or public scandals. That keeps the search focused on misconduct-related signals.
3. It tells the model where to look
By naming sources like news reports, court records, public complaints, regulatory filings, and reputable websites, the prompt encourages stronger evidence. This matters because fraud accusations are high risk and easy to get wrong.
4. It breaks the task into research categories
The bullet points act like a checklist:
- scam or fraud allegations
- scandals or legal proceedings
- victim complaints
- regulatory or criminal records
- context separating fact from rumour
That structure helps the model avoid missing key angles and makes the final output more complete.
5. It handles name ambiguity
This is one of the most important parts. If the name is common, the model must first figure out which specific person is being discussed. Otherwise it could wrongly attach one person’s scandal to another person with the same name.
6. It forces distinction between facts and claims
The prompt explicitly asks to separate:
- confirmed / verified information
- allegations / unverified claims
That is critical for fairness and accuracy. A complaint, accusation, or blog post is not the same as a court finding or official charge.
7. It reduces defamation risk
The instruction “If no credible evidence of wrongdoing exists, state that directly” is a safeguard. It tells the model not to invent suspicious narratives just because the user asked.
8. It asks for an objective tone
That helps prevent sensational language. For a topic like fraud or scandal, tone matters because emotionally loaded wording can make weak claims sound proven.
9. It asks for citations
“Cite your sources where possible” improves transparency. It lets the reader verify the claims instead of just trusting the output.
What this prompt is really doing is telling the model to follow a process like this:
- identify the correct person
- gather evidence from stronger sources
- compare claims across sources
- separate verified facts from accusations
- report cautiously and objectively
A stronger version would replace NAME consistently throughout the prompt and avoid mixing it with “Troy Douglas,” because right now one part says NAME and another bullet specifically says “Troy Douglas.” That could confuse the model.