To get beyond generic advice, instruct ChatGPT's voice mode to act as a challenging mentor. Prime it with a specific framework like the Theory of Constraints (TOC) and provide your resource limitations. This structured dialogue forces the AI to challenge your assumptions and generate realistic, actionable solutions instead of pleasantries.

Instead of prompting a specialized AI tool directly, experts employ a meta-workflow. They first use a general LLM like ChatGPT or Claude to generate a detailed, context-rich 'master prompt' based on a PRD or user story, which they then paste into the specialized tool for superior results.

Most people use AI to perform tasks like writing copy. A more powerful application is using it as a strategic brainstorming partner. Ask it high-level questions about cultural trends and consumer behavior (e.g., 'Why did this artist pop?') to generate novel insights for your strategy.

Even professionals who use ChatGPT daily are often unaware of its most powerful "reasoning" capabilities, like Deep Research. This pervasive knowledge gap means users stick to basic tasks like writing, missing out on the profound strategic value these advanced features offer for complex problem-solving.

Treat AI as a critique partner. After synthesizing research, explain your takeaways and then ask the AI to analyze the same raw data to report on patterns, themes, or conclusions you didn't mention. This is a powerful method for revealing analytical blind spots.

The most effective way to use AI is not for initial research but for synthesis. After you've gathered and vetted high-quality sources, feed them to an AI to identify common themes, find gaps, and pinpoint outliers. This dramatically speeds up analysis without sacrificing quality.

The effectiveness of AI tools like ChatGPT depends entirely on the quality of the initial inputs. To get exceptional results, "brief" the AI by uploading foundational documents like your company manifesto, jobs-to-be-done, and brand positioning. A lazy or generic prompt yields generic results.

AI models tend to be overly optimistic. To get a balanced market analysis, explicitly instruct AI research tools like Perplexity to act as a "devil's advocate." This helps uncover risks, challenge assumptions, and makes it easier for product managers to say "no" to weak ideas quickly.

Instead of asking one-off questions, build a detailed, pre-written prompt (a "shortcut") within an AI browser. This standardizes your analysis framework, allowing you to instantly reverse-engineer any company's marketing strategy with a single command, making deep research scalable and repeatable.