Lesson Content

The difference between AI assumption testing and research-backed assumption testing.

Most AI chatbots test assumptions through reasoning – "here is what is typically true in situations like this." Perplexity adds real-world evidence: "here is what research and real-world cases actually show about this assumption."

This difference matters for high-stakes planning. Research-backed assumption testing is more reliable than reasoning from general patterns.

The assumption identification step.

Before researching, identify your assumptions:

"I have a plan: [describe plan]. What assumptions does this plan depend on being true? List them in order of: (1) most critical to plan success, (2) most uncertain, (3) most difficult to verify."

This gives you a prioritized assumption research list.

The assumption evidence search.

For each critical assumption:

"Research whether this assumption is supported by evidence: [state assumption]. What do real-world cases, studies, or data show about whether this is typically true in [your context]? What would need to be true about my specific situation for this assumption to hold?"

Distinguishing types of evidence.

Evidence quality matters:

• Controlled studies / peer-reviewed research: strongest general evidence
• Multiple independent case studies: strong contextual evidence
• Industry surveys or aggregated data: moderate evidence
• Individual cases or anecdotes: weak – directional only
• Expert opinion: valuable for interpretation – not substitute for evidence

When assumption research is insufficient.

Some assumptions can only be validated through direct investigation of your specific situation – talking to actual customers, testing at small scale, or consulting domain experts who know your context. Research tells you what is generally true; only real-world investigation tells you what is true for your specific case.