An AI Glossary for Owner-Operators

If you’ve experimented with tools like ChatGPT or Claude for your operation, you’ve likely encountered the vocabulary for talking about artificial intelligence: prompting, LLMs, MCPs, agents. New terms are springing up as AI tools and practices continue to evolve, and at Ambrook we’ve found it helpful to familiarize ourselves with the terminology as we learn the tech.

In this lesson, we’re sharing terms we’ve used to talk about AI with each other and with Ambrook customers. For each one you’ll see a definition and examples of how it might apply for owner-operators in farming, ranching, construction, trucking, or property management.

Key Takeaways

This glossary covers 13 AI terms (or sets of terms) you may have encountered, described in plain English with examples. We’ve organized them into four groups.

Talking about AI infrastructure:

• Artificial intelligence (AI) and generative AI

• Large language models (LLMs)

Talking about AI tools:

• AI assistants

• Workflows

• Skills

• Agents

Talking about interactions with AI:

• Prompts

• Context

• Vibe coding

• Hallucinations

Talking about AI’s access to data:

• Connectors and MCP (Model Context Protocol) servers

• Read and write access

• Training settings

Talking About AI Infrastructure

Artificial Intelligence (AI) and Generative AI

Artificial intelligence (AI) is software that finds patterns in large amounts of data and learns from them. Unlike other kinds of automations, it doesn’t follow strictly fixed rules, so it can get better at a task or action as it gets trained on more data.

Here’s an example of the difference:

• In Ambrook, automations help you tag transactions by following a fixed rule you’ve set. You tell the system to always look for fuel charges with the same description, for example, and tag them to your “Fuel” category.

• Ambrook’s AI category suggestions aren’t following any particular rule you’ve laid out. The system looks at how you’ve historically tagged many different types of transactions. It finds the patterns in your tagging behavior, learns from that data, and suggests more accurate categories to you over time.

Generative AI is AI that can create something new based on your request. It can produce a piece of writing (like an answer to a question or a report), a spreadsheet, an image. Tools like ChatGPT or Claude are examples of generative AI. You may have already used generative AI to do things like:

• Summarize your operation’s current AR/AP

• Plan projects or tasks, like grazing rotations or cattle sales

• Double-check your ledger for record-keeping errors

Large Language Models (LLMs)

Large language models (LLMs) are the type of AI that powers tools like ChatGPT and Claude. They’re software algorithms that are trained to read large amounts of natural language (like human writing), so they can predict what the next word in a sentence should be based on the patterns they see. That’s how they formulate written answers to a user’s requests; you’re interacting with LLMs anytime you ask ChatGPT or a similar tool a question.

Many operators have noticed that AI companies often release new models. For example, Claude offers models including Opus, Haiku, and Sonnet, and lets you choose the one you want to use. This is because some models are specifically designed to be better at responding to certain kinds of requests, like more complex work vs. simpler questions.

Talking About AI Tools

AI Assistants

AI assistants generally describe the actual app or interface you’re interacting with. ChatGPT, Claude, or Grok are all examples of AI assistants you may have used to analyze operational or financial data or scenarios for your business. They run on the LLMs we talked about earlier.

Workflows

An AI workflow is a series of steps that you ask AI to complete. It’s more complex than a typical question-and-answer in your chat, since you’re having AI go through multiple tasks. But you usually get a consistent result, because you’re laying out what AI should do.

Here are examples of AI workflows you might have created or used before:

• You uploaded PDFs of a few recent bills, then asked AI to read the line items and suggest the enterprises in Ambrook that you should tag them to.

• You asked AI to read your ledger, unpaid invoices, and open bills in Ambrook, then draft a brief summary of the last month’s cash flow and AR/AP.

• You told AI what types of funding you’d like to apply for this year, then asked it to search the web for grants that would fit your operation’s projects and location, and return them to you in a ranked list.

Skills

AI skills are workflows that you save, like a set of reusable instructions that ChatGPT and Claude can reference every time you have a similar request. You can create a skill by explicitly telling AI, “Create a skill that does [x]” or “Turn the workflow you just did into a skill,” and chatting with the AI assistant to fill in the details it needs.

In both ChatGPT and Claude, AI will ask you to name your skills and you’ll be able to call them in a chat going forward. For example in Claude, you’d type /skill-name to use a skill; in ChatGPT you’d type @skill-name.

Skills are helpful when there’s an AI workflow you expect to use over and over again. You could have skills for recurring flows like:

• Each month, AI helps you find reconciling items between your ledger and account statements.

• Every two weeks, AI produces a report summarizing your recent cash flow, AR/AP, and untagged transactions.

• Whenever you want to clean up your bookkeeping structure, AI audits your list of categories in Ambrook and flags ones that could be merged or edited.

To learn more about creating AI skills, check out our lesson on building a financial health report in ChatGPT.

Agents

An AI agent is a system that’s designed to work independently on complex tasks, which could be open-ended and have a less predictable result. Instead of defining the steps like you would for a skill or workflow, you give an agent a goal. It then decides what information it needs, which steps to take, and which skills it should use to get there.

You might have noticed that ChatGPT or Claude acts like an agent sometimes, depending on your request. This can happen when you give it a larger task that doesn’t have defined steps, and it needs to plan and figure out how to do what you asked. For example, maybe you asked it to:

• Build a web dashboard to help you track crew schedules across job sites

• Create an app to help you manage rotational grazing

• Come up with a system to help you handle rental property maintenance requests

Talking About Interactions with AI

Prompts

A prompt is what you type, say, or upload to tell an AI assistant what you’d like it to do. When a prompt gives enough detail and background on the request, AI is more likely to produce an accurate and useful result. In our lesson on building financial health reports with AI, you can learn more about four elements we use at Ambrook to write clear prompts (persona, task, context, and format).

But depending on the complexity of your request, prompts could be as simple as:

Can you check how many unpaid invoices I have in Ambrook and who the customers are?

They can also contain more detail up front, especially if you have specific data sources you want AI to search or a particular format you want it to present results in:

I’m the owner of [business name]. We do [enterprises] in [location]. I want to track [business goal, metric, or data]. In Ambrook, look at [data sources] then show me [results] in [format].

Context

Context describes all the information AI has access to when it’s working on your request. That includes your prompts, AI’s replies, files you’ve uploaded to the chat, and data it may have pulled from connected apps like your bookkeeping software. Anytime you tell AI about your business, you’re adding to its context for that particular chat.

Vibe Coding

When operators build apps, dashboards, or other software using AI and without needing to code, they’re vibe coding. You describe what you want to create in plain English and let AI handle the technical tasks. As one example, we wrote about how Jim Eitel, Texas rancher and Ambrook team member, built a command center for his farm.

Vibe coding can be useful if operators want to build a custom internal tool for their business; maybe there are certain tasks you need to track but can’t find existing software for, or you want a tool to help you combine your operational and bookkeeping data in very specific ways. We’ve seen owners use platforms like Claude, ChatGPT, Lovable, Replit, and Base44 to create custom operational tools that work alongside their books.

Note: Anytime you use AI, but especially when building custom software, remember to take the steps to protect your data, use the technology safely, and always check and test AI’s work. We share more safety and privacy tips in our first lesson on AI for financial management.

Hallucinations

A hallucination is when AI states false information. This can happen when the LLM predicts the next word in a sentence without grounding its response in a real data source. It’s why operators always need to review AI’s answers or work before using it.

Here are a few ways you can try to minimize hallucinations, identify inaccurate information in an AI response, and double-check AI’s work:

1. Be as specific as possible in your prompts. For example, tell AI which data sources to use if there are particular ones you trust; narrow down the region, time period, or task it should look at.

2. Ask AI to cite its data sources and outline exactly how it arrived at its answer.

3. Tell AI to explicitly state when it isn’t sure about a piece of information or source.

4. Pick a number or data point you know you can manually verify, without using AI. Compare your work against AI’s work.

5. Draw your own conclusions and always make final calls on business decisions yourself. Use AI as a tool to help gather what you need to make a more informed decision.

We share some more tips on evaluating AI’s work in our piece on farm financial planning with AI.

Talking About AI’s Access to Data

Connectors and MCP (Model Context Protocol) Servers

The Model Context Protocol (MCP) is a standard way for AI tools to connect to other software. For example, if your bookkeeping platform has an MCP server, you can set up a connection that lets ChatGPT or Claude access and read your books directly. In AI tools, that’s often called a connector (or sometimes a plugin).

You’ll typically set up a connector in an AI tool’s settings or a section specifically for connectors. For example, we wrote about how to install the Ambrook MCP in ChatGPT.

Once it’s connected, you can ask ChatGPT or Claude questions about your books like:

• How much did we spend on truck washes in September?

• Between October 1 and October 15, which of my transactions are missing an enterprise tag?

• Do I have any categories with zero transactions tagged to them?

Read and Write Access

Typically, a connector can give your AI tool read access or write access (or both) to software it’s connecting to. Let’s say you’ve connected your bookkeeping platform to Claude: Read access means Claude can look at the data in your bookkeeping tool to answer questions about it, but AI can’t make any changes to the data. Write access means Claude has the ability to make changes in your bookkeeping tool; that could mean following your instructions to update specific records, for example, or tag certain transactions.

Note that not all MCP servers give write access in the first place. Currently Ambrook’s MCP only allows AI to read your books. Regardless, it’s important to know what level of access you’re giving AI when you connect any external app:

• Are you letting it read, write, or both?

• What kinds of data from connected apps are you allowing it to read or write?

You can check these access permissions at any time by going to the connector or plugin’s settings in ChatGPT or Claude.

Training Settings

Training settings control whether the company that built your AI assistant can use your chats and files to train its technology. We recommend disabling these settings as you’re using AI to help plan or analyze information related to your operation.

In tools like ChatGPT and Claude, look for settings to “Improve the model for everyone” or “Help improve AI models.” And even with training settings turned off, avoid sharing highly sensitive or personal details about you, your operation, or those you work with in an AI chat.

Resources to Dive Deeper

• Learn about 5 ways AI can help with bookkeeping tasks.

• Follow our guides to building financial health reports and a ledger review workflow with AI.

• Read how we’re using AI internally at Ambrook.

FAQs

What’s the difference between ChatGPT, Claude, and Gemini?

ChatGPT, Claude, Gemini, Grok, and similar tools are AI assistants built by different companies. They use different language models, some of which are designed to be better at certain types of tasks. Some operators may try multiple AI assistants and find that one is more efficient or accurate for their usual requests.

Is it safe to connect AI to my accounting software?

You can take steps to safely connect external apps (like your accounting software) to AI. For example:

• As you set up the connector, always double-check the level of access you’re giving AI to the software: Is it read access, write access, or both?

• When you’re asking AI questions about your books, be specific about what you’re looking for to minimize the risk of inaccurate information.

• Use AI with a private account and enable multi-factor authentication for security.

• In any AI chat, avoid sharing highly sensitive information about yourself, your operation, or the vendors and customers you work with.

We share more tips on using AI safely in our lesson on building financial health reports.

Does AI train on my data?

It depends on the tool, your plan, and your settings. In tools like ChatGPT and Claude, you can turn off settings that allow AI companies to train their technology on your data.

Do I need to know how to code to build apps with AI?

We’ve seen many operators experiment with vibe coding, where they describe custom, internal apps they want to build in plain English and let AI handle the coding itself. For example, you can read about how Jim Eitel built his farm command center with AI.

Like any time you ask AI to analyze or build something with data about your operation, avoid sharing highly sensitive information and always check AI’s work before using it.

What’s the difference between AI and rule-based automations?

Rule-based automations follow exactly the same set of instructions every time, like tagging transactions with the same description to the same category. AI adapts to patterns in large datasets, meaning some AI tools can improve as they learn from more data.

This resource is provided for general informational purposes only. It does not constitute professional tax, legal, financial or accounting advice. Consult a qualified professional about your specific situation.