Practical prompting, backed by research

The paradox: Give a model search access and accuracy on answerable questions goes up 12%, but its ability to say "I don't know" on unanswerable ones drops 7%. ChatGPT with web search sees "Who will be US president in 2075?" and instead of "nobody knows" fires off 10+ search queries, burns 2,000 tokens, and still answers wrong or vaguely. The method shows you when the model is wasting tokens hunting for an answer that doesn't exist, and how to teach it to decline. The catch: corpora describe what we know, not what we don't. The model searches "president 2075" → finds forecasts, statistics, opinions → reads the lack of a precise answer as "I didn't search hard enough, try again". Tokens per correct answer: 382 without search, 765 with search — twice as expensive for the same accuracy.

An LLM answers part of a complex question and forgets the rest. You ask for a comparison of two things — you get details on only one. The reason: the model doesn't track what's already known and what isn't yet. SEA lets you get complete answers to multi-step analytical tasks — competitor comparisons, market research, synthesizing facts from different fields. The method turns the completeness check into an analytical checklist. The model, as a Strategic Intelligence Analyst, breaks the question into a list of Required Findings (what needs to be known), checks them against Confirmed Findings (what's already there), and explicitly names the Remaining Gaps (what's missing). The gaps become the basis for new targeted questions — the loop repeats until the checklist is fully closed.