We're not facing artificial general intelligence. We're interfacing with sophisticated language models that can extend how we think, if we design the connection right.
Correct the map
If we call everything “AI, ” we blur what's actually happening: language in, language out, with powerful patterns in between. Treating a model as a cognitive extension clarifies your job, build a clean interface between your intent and its output, then keep that interface honest over time.
Take a simple case. A sales ops lead asked a model to “take over my email.” Responses sounded generic and off-brand. After reframing the task as extension, she wrote: “Draft three replies that match my tone, curt but helpful, each under 120 words, with one question that moves the deal forward.” The drafts became usable because the interface carried her intent.
When you correct the map, you stop chasing magic and start designing the bridge.
Architect identity interfaces
With the map corrected, the next question is how you stay coherent as you extend. Identity doesn't hold by isolation; it holds when your values, reasoning patterns, and goals shape the tool's behavior.
Think of an “identity mesh” as the light scaffolding that keeps your voice intact. Define your tone, your non‑negotiables, and your preferred patterns of reasoning. Put them in a small “coreprint”, a compact, plain‑language statement the model sees before it generates. Add a “resonance band”: a short list of what “on‑tone” looks like, so you can evaluate outputs quickly.
Example: a policy analyst created a coreprint with three anchors, avoid certainty language, cite trade‑offs, and show two alternatives before a recommendation. By pasting this ahead of every ask, she stopped getting absolute answers and started getting structured, on‑identity drafts.
Run public experiments
Once the interface reflects your coreprint, the real learning comes from friction in use. Static theory won't surface the edge cases; small, visible experiments will.
Treat each interaction as field data, not a one‑off. Log the prompt, the output, what you kept, and what you changed. Patterns emerge quickly, where the model hallucinates, where it shortens too much, where it mimics you too closely.
A product designer ran a two‑week sprint: daily 20‑minute sessions, with a single task, rewrite usability notes into “decision memos.” She kept a simple change log: prompt, output, edits, final. By day four, she learned that asking for “rationale before recommendation” cut her editing time by half because the model stopped burying trade‑offs.
Tighten semantic anchors
Experiments only teach if your words actually carry your intent across the boundary. Because this is a language interface, precision in terms becomes the mechanism of control.
Create “semantic anchors”, short, unambiguous phrases that compress complex instructions. Anchors act like handles your strategic self can grab with minimal overhead, forming a repeatable framework loop between you and the model.
Here's one micro‑protocol to build anchors fast:
1) Collect the phrases you keep re‑typing in edits; these are the real requirements. 2) Name each requirement with a two‑ to three‑word anchor (e.g., “rationale‑first, ” “risk ledger, ” “tone‑tight”). 3) Define each anchor in one sentence with a falsifiable test (e.g., “rationale‑first: state the why in the first two sentences, or it fails”). 4) Put the anchors at the top of your prompts and in your reviews, and track which ones reduce edits.
Example: a PM shifted from “write a PRD” to “PRD v1, rationale‑first, risk ledger, 2 alternatives before pick.” She added simple tests (“risk ledger: list 3 risks, each with mitigation”). Outputs stabilized, review time dropped, and the team shared the same context map without longer prompts.
Practice conscious co‑authorship
With precise language in play, the tool begins to shape you back. That's not a problem if you maintain a metacognitive control layer, a simple habit of noticing which patterns you're adopting and whether they serve your trajectory vector.
Two checks make this practical. First, apply “signal discipline”: keep what clarifies, strip what dilutes. Second, schedule a brief weekly audit comparing your native drafts to model‑touched drafts. You're looking for creep: cadence, hedging, or formulaic structures sneaking into your coreprint.
A writer noticed her sentences getting longer after two weeks of heavy use. She ran a side‑by‑side: 10 native sentences vs. 10 model‑touched. She then added a counter‑anchor, “short lines, one idea”, and pruned the model's habits from her identity mesh. Output stayed augmented; voice stayed hers.
You shape the extension, and it shapes you; by design beats by default.
Start a two‑week pilot, define a coreprint, run a small experiment, install three anchors, and share your trajectory proof with a colleague tomorrow.
Here's a thought…
Create a “coreprint” for your next AI interaction: write 2-3 sentences defining your tone, non-negotiables, and reasoning patterns, then paste it before your prompt.
