Growing Your Lexicon with AI

Most professionals stop learning words the way they stop learning songs. Nothing dramatic happens. The habit just fades, and one day the vocabulary you use in a room is the vocabulary you had years ago.

Why it matters

A consultant sells precision. The client feels that something in the process is broken, and the job is to name it so exactly that they nod. Vague words produce vague scopes. Vague scopes produce change orders.

I have sat on both sides of that table. On the shop floor I lived with the results of recommendations written by people who never found the right word for what they saw. Later, writing those recommendations myself, I learned how easy it is to reach for a word that is close and call it done.

Close is expensive. The gap between “inefficient” and “redundant,” between “complex” and “convoluted,” is the gap between a problem the client ignores and one they fund.

Every room has its own language

In a single week I can sit with a controller, a warehouse supervisor, and a field service manager, and hear the same two letters mean three different things. To finance, a PO is a commitment. To receiving, it is a document taped to a pallet. To a project lead, it is the thing that has not been approved yet.

Industry shifts the meaning again. A service order in a manufacturer’s warehouse is not the service order in a field operation. Departments inside one company carry dialects of their own, and nobody announces which one you are hearing. They assume you speak it.

The consultant who nods and moves on builds the solution on a guess. The one who stops and asks learns that two departments have used one word for two different things for years, and that the gap between them is the real problem.

That habit of stopping is what the rest of this post is about.

The problem with looking a word up

Everyone has done this. You meet a word, open a tab, read the definition, nod, and close the tab. Two days later the word comes back and the definition is gone.

A lookup answers the question and throws the answer away. Nothing about the word is kept, and nothing ties it to the work you do. The word stays foreign because you never used it once.

What a lookup lacks is a place for the word to live and a reason to return to it.

The workflow

The process has four steps, and the AI carries most of the weight.

1. Ask. When a word stops you, in an email, a contract, or a client’s own description of their problem, ask Claude what it means. Plain language, one sentence.
2. Read the nuance. The definition is the smallest part of the answer. Ask for the origin, the register, and the near words it gets confused with. Is it formal or archaic? What would a careful writer use instead?
3. Save it. I built a skill that logs the word to a Notion database with the definition, the part of speech, and four example sentences. I copy nothing by hand. I ask once and the entry exists.
4. Return. This is the step most people skip. The database sorts by date added, so a quick scan of recent words, followed by using one on purpose in a message or a call, turns a record into a habit.

The skill matters more than the model. Any good model can define a word. A skill makes the definition land in the same place, in the same shape, every time, and that is the only reason a habit survives a busy week.

One question becomes one record, and the record sharpens what you notice in the next room.

The skill

The skill is short on purpose. Here it is, with my database link and IDs removed. Copy it, point it at your own database, and change the examples rule to fit the work you do.

---
name: define-word
description: Define a word and log it to a Notion Definitions database with the definition and four example sentences. Use when asked what a word means.
---
# Define a word
When asked for a word (or short phrase) to be defined, do two things: answer in the chat, and record the entry in the Notion Definitions database. Both, every time. The database entry is not optional and is not something to offer or ask about.
## The Notion target
- Database: Definitions
- Properties: Word (title), Definition (text), Part of Speech (select: noun, verb, adjective, adverb, pronoun, preposition, conjunction, interjection, phrase), Example 1 to Example 4 (text), Date Added (created time, automatic, never set it)
## Steps
1. Check for a duplicate. Query the database for an existing row whose Word matches. If one exists, say so and show the existing entry rather than creating a second row. Only add a new row for a different sense of the word.
2. Write the entry. Create the page in the Definitions database.
3. Answer in chat. Give the definition, any useful nuance, and the four examples as a numbered list. Close with a link to the new row.
## Content standards
- Definition: one sentence, plain language, no circular phrasing. Put etymology, register, and commonly confused near words in the chat reply, not in the Definition property.
- Part of Speech: pick the sense being asked about. If the word works as more than one part of speech and the context does not settle it, define the most common sense and mention the other briefly in chat.
- Examples: exactly four, each a complete sentence using the word naturally. Vary the grammatical position across the four (subject, object, modifier, predicate) rather than reusing one frame. Draw at least one or two from your own work, so the word is usable where you will actually use it. Avoid dictionary stock sentences.
- If the word is obscure, archaic, or a technical term of art, say so in chat. That context matters more than the definition itself.
## Multiple words
If several words are asked about at once, create all the rows in a single call, then answer each word in turn.

The examples rule is the part worth customizing. Mine asks for sentences from consulting and delivery work. Yours should ask for sentences from the rooms you actually sit in.

Why four examples

The skill writes four sentences for every word, and each is built differently. The word is the subject in one, the object in another, a modifier in the third, and part of the predicate in the fourth. At least one or two come from my own work: discovery calls, solution design, a Power Platform delivery that slipped.

A definition tells you what a word means. An example shows you where it fits. Four examples in four positions teach the grammar of the word, which is what you need when you reach for it in the middle of a sentence that is already moving.

Sentences from your own world do the rest. A word you can picture saying to a client is a word you will say.

What changes over time

Keep it up for a few weeks and the database stops being a list and becomes a record. You can see which words you reached for, and what they say about the problems you were describing that month.

The second change is quieter. You start noticing words before they stop you. A term you saved weeks ago turns up in a proposal someone else wrote, and you recognize it without opening a tab. That recognition is the lexicon growing.

The AI does not read for you. It removes the excuse to skip the stop.

Start your own

You need three things. A database with columns for the word, its definition, its part of speech, and four examples. An AI that will define a word on request. And one rule: every word you ask about gets saved. Build that rule into a skill or a saved prompt so you never have to decide it twice.

Then wait for the next word that stops you.