01.2 - 1 (twelve) 12 Terms You Need Now
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- Chatbot: An AI you talk to. It answers, then waits for the next message.
- Agent: An AI that can work through a task. It plans, uses tools, checks the result, and adjusts.
- Worker: Our name for an agent with a standing job. A worker has a clear responsibility, a trigger, tools, limits, and a way to report back.
Terms
1. Model
- What it means: the trained AI engine that reads your request and produces a response.
- Why it matters: different models have different strengths, costs, speed, and reliability.
- Where it shows up: when you choose what your worker thinks with. Examples include Claude, GPT, Gemini, and other model providers.
2. Prompt
- What it means: the instruction and background you give the model.
- Why it matters: a vague prompt creates vague work. A clear prompt gives the worker a job, standards, and boundaries.
- Where it shows up: when you write directions like, "Summarize today's inbox and flag anything that needs me."
3. Output
- What it means: the answer, draft, file, summary, or action the model produces.
- Why it matters: you need to judge the output, not just the conversation. Good workers produce something useful.
- Where it shows up: when your worker sends a daily brief, creates a draft reply, updates a sheet, or reports a blocker.
4. Context
- What it means: everything the model can see while it works: your request, notes, examples, files, previous messages, and data it just pulled in.
- Why it matters: context is what makes a worker specific to you instead of generic.
- Where it shows up: when you give the worker your business notes, client rules, inbox examples, or project brief.
5.Context window aka length??
- What it means: the model's temporary workspace. Only a certain amount of text and data fits inside it at one time. MD Files come into play here?
- Why it matters: if the window fills up, old or less relevant material may fall out, which can make the worker forget details during a long task.
- Where it shows up: when long chats, big documents, and too many examples make the worker slower or less accurate.
6. Memory
- What it means: information the system saves between sessions, such as facts about you, your business, preferences, and recurring workflows.
- Why it matters: memory stops your worker from starting from zero every time.
- Where it shows up: when the worker remembers your tone, your company names, your client rules, or your recurring tasks
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7. Session
- What it means: one continuous work thread with the agent, from start to finish.
- Why it matters: sessions keep context focused. Starting fresh when you switch tasks helps avoid confusion.
- Where it shows up: when you finish one task, start a new one, or ask the worker to continue the same job
8. Tool
- What it means: something the worker can use to act on the world, such as email, calendar, browser, Drive, Sheets, or a script.
- Why it matters: without tools, the AI can only talk. With tools, it can do work
- Where it shows up: when the worker reads email, checks a calendar, searches the web, edits a document, or updates a database.
9. Integration or API
- What it means: the connection that lets programs talk to each other. An integration is the user-friendly version. An API is the technical doorway underneath it.
- Why it matters: workers become valuable when they can reach real business systems instead of relying only on pasted text.
- Where it shows up: when your worker connects to Gmail, Slack, Stripe, HubSpot, Google Drive, or a custom business system.
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shift number goes reverse
10. Trigger, workflow, and automation
- What it means: a trigger starts the job, a workflow is the sequence of steps, and automation means the job runs without you manually doing it each time.
- Why it matters: this is how you turn AI from a helper into an operating system.
- Where it shows up: when the worker runs every morning, watches for a new email, posts a report, or starts when you text it.
11. Guardrail, permission, and human approval
- What it means: the rules that say what a worker may do alone and what must wait for you.
- Why it matters: autonomy without limits is risky. Good workers move fast on safe tasks and pause before sensitive ones.
- Where it shows up: when a worker drafts an email but asks before sending, flags a payment before approval, or refuses to expose private data.
12. Logs, tests, and iteration
- What it means: logs record what happened, tests check whether the worker works, and iteration means improving it after each run.
- Why it matters: reliable workers are built by checking results and tightening the system, not by hoping the first version is perfect.
- Where it shows up: when you review the daily brief, fix a missed rule, add a better example, and run the worker again.
** Implement % first digit place navigation
0 twice 10 three time in row 20 , same for all digits 1->12-21, ... 9, 9->19->19
shift number goes reverse