| Before the request | Truthwhat can be asked | E-T1Asks at random; expectations are polar — “AI can do everything” or “AI is a toy.” | E-T2Sticks to 2–3 task types that once worked; never probes new ones. | E-T3Keeps a map of own tasks marked delegable / partial / not — and can explain it. | E-T4Predicts the odds of success for an unfamiliar task in advance — and the predictions hold. | E-T5Systematically probes new classes of questions; findings flow to colleagues. |
| Deeptask boundaries | E-D1States the task in one phrase, however it comes out; sees no boundaries. | E-D2Has noticed that some phrasings work better — without understanding why. | E-D3Decomposes the task; draws the frame explicitly: what is in, what is out, which assumptions. | E-D4Deliberately moves the boundaries — narrows, widens, tests the frame's edge cases. | E-D5Transfers the framing skill to new domains; helps others formulate. |
| The request | Connectcontext | E-C1Gives no context — expects the model to “just know” the company and the project. | E-C2Pastes fragments of documents when remembered; context composition is accidental. | E-C3Knows which context each task type needs; prepares it in advance — role, data, examples. | E-C4Doses, refreshes, and prunes context; understands how its composition shifts the answer. | E-C5Builds reusable context kits — task profiles, example sets — for self and team. |
| Serviceprompt & system choice | E-S1One request style for every system; sees no differences between models. | E-S2“This one is better for code” — choice by habit, never tested. | E-S3Deliberately picks the system and mode per task; knows the strengths and limits of those available. | E-S4Compares systems on own tasks; adapts the prompt to the specific model. | E-S5Tracks model changes, updates own practice, advises colleagues on choice. |
| After the response | Knowledgeworking the answer | E-K1Copies the answer as-is, or discards it whole. | E-K2Edits by hand; occasionally asks to “rewrite it shorter.” | E-K3Commands a set of transformations: format, tone, structure, audience. | E-K4Builds processing chains: draft → critique → rework → final form. | E-K5Keeps a library of output formats; the model's answer is raw material in a tuned personal process. |
| Evolutioniteration | E-E1One request — one answer; a failure means “AI can't do this.” | E-E2Sometimes re-asks — unsystematically, and quits fast. | E-E3Budgets 2–3 refinement cycles as the norm, not as a breakdown. | E-E4Manages the cycle: knows when to refine, when to restart, when to stop. | E-E5Extracts lessons from iterations and updates own kits — future cycles get shorter. |
| Cross-cutting | Responsibilityvalidation | E-R1Takes it on faith, or rejects it by gut feel; no checking. | E-R2Checks “when it matters” — with no criteria for what exactly to check. | E-R3Knows the typical failures — hallucinations, stale data; verifies facts and figures; knows where checking must stay human. | E-R4Holds a validation protocol per task type; uses the model for self-checks — knowing the limits of that move. | E-R5Formally separates delegable from non-delegable responsibility; results survive external audit; teaches others. |