AI excels at clerical tasks like transcription and basic analysis. However, it lacks the business context to identify strategically important, "spiky" insights. Treat it like a new intern: give it defined tasks, but don't ask it to define your roadmap. It has no practical life experience.

To get unbiased user feedback, avoid asking leading questions like "What are your main problems?" Instead, prompt users to walk you through their typical workflow. In describing their process, they will naturally reveal the genuine friction points and hacks they use, providing much richer insight than direct questioning.

To trust an agentic AI, users need to see its work, just as a manager would with a new intern. Design patterns like "stream of thought" (showing the AI reasoning) or "planning mode" (presenting an action plan before executing) make the AI's logic legible and give users a chance to intervene, building crucial trust.

A powerful workflow is to explicitly instruct your AI to act as a collaborative thinking partner—asking questions and organizing thoughts—while strictly forbidding it from creating final artifacts. This separates the crucial thinking phase from the generative phase, leading to better outcomes.

After an initial analysis, use a "stress-testing" prompt that forces the LLM to verify its own findings, check for contradictions, and correct its mistakes. This verification step is crucial for building confidence in the AI's output and creating bulletproof insights.

A powerful and simple method to ensure the accuracy of AI outputs, such as market research citations, is to prompt the AI to review and validate its own work. The AI will often identify its own hallucinations or errors, providing a crucial layer of quality control before data is used for decision-making.

A key principle for reliable AI is giving it an explicit 'out.' By telling the AI it's acceptable to admit failure or lack of knowledge, you reduce the model's tendency to hallucinate, confabulate, or fake task completion, which leads to more truthful and reliable behavior.

Many users know about AI's research capabilities but don't actually rely on them for significant decisions. A dedicated project forces you to stress-test these features by pushing back and demanding disconfirming evidence until the output is trustworthy enough to inform real-world choices.

Hunt's team at Perscient found that AI "hallucinates" when given freedom. Success comes from "context engineering"—controlling all inputs, defining the analytical framework, and telling it how to think. You must treat AI like a constrained operating system, not a creative partner.