GPT 4 is Smarter than You Think: Introducing SmartGPT

SmartGPT’s core claim is that GPT-4’s benchmark performance can be materially improved—not by changing the model, but by wrapping it in a multi-step prompting and self-checking workflow. The approach combines (1) a stronger “let’s work this out step-by-step” prompt, (2) generating multiple candidate outputs under controlled randomness, (3) prompting the model to reflect on errors, and (4) running a short “researcher → resolver” dialogue to select a final answer. The practical payoff is that many of GPT-4’s mistakes—especially those that are detectable after the fact—can be corrected before the user ever sees the result.

The video opens with a concrete example from a TED Talk drying-clothes puzzle: “If it took five hours to dry five clothes, how long to dry 30 clothes?” GPT-4’s standard response lands at 30 hours, while SmartGPT consistently produces the correct 25-hour answer and includes the assumptions behind its reasoning. That sets up the broader argument: published benchmark scores may understate GPT-4’s real capability because they often measure a single-shot response rather than a system that can verify and revise its own work.

SmartGPT is built around three prompting improvements that have recently shown measurable gains. First is step-by-step prompting, where the model uses the input token space for intermediate computation rather than relying solely on internal activations. Second is reflection: by asking for error-spotting, GPT-4 sometimes identifies flaws in its own draft answers. Third is dialogue-based resolution: multiple outputs can be compared, and a final “resolver” can choose the best option after the “researcher” stage surfaces reasoning and potential issues.

To test the idea, the creator manually ran many trials on selected slices of the MMLU benchmark, focusing on topics expected to be difficult for GPT-4—such as formal logic. In a small formal-logic subset, direct zero-shot prompting produced about 68% accuracy. Adding step-by-step prompting raised it to roughly 74–75%. The full SmartGPT pipeline, using reflection and the researcher/resolver selection step, reached about 84%. Across tests, the pattern was consistent: the resolver corrected roughly half of the errors GPT-4 made under the baseline setup. The video also reports that SmartGPT’s gains were weaker on tasks involving counting, division, multiplication, or character-level details—cases where the model’s wrong answer may be too subtle for self-critique to catch.

The video then connects these improvements to broader stakes. An AI governance researcher’s heuristic ties an MMLU score near 95 to “agi-like” abilities. Since GPT-4’s reported MMLU score is 86.4, correcting about half of errors could plausibly push performance into the low 90s—potentially narrowing the gap toward that 95 threshold. The creator also cites recent papers that optimize prompts via automatic prompt engineering and show large gains from step-by-step reasoning, while noting that researchers still lack a full theoretical explanation for why these prompting techniques work.

Finally, the video outlines future upgrades: more generic few-shot examples, longer and richer multi-agent dialogue, tuning sampling temperature across stages, and integrating tools like calculators or code interpreters to eliminate arithmetic and counting failures. The overall message is less about “smarter GPT-4” and more about “smarter usage”—a system design that extracts more reliable reasoning from the same underlying model, while raising questions about how thoroughly model vendors test capabilities before release.