Mountainside Media

Data Driven Marketing for Service-based Businesses

AI Terms for Service Providers

A practical glossary for making sense of AI in your business

AGI (Artificial General Intelligence)

What is it:
A hypothetical form of AI that can perform any intellectual task a human can, including hard-to-replicate traits like common sense reasoning, self-awareness, and moral judgment. Unlike today’s tools, AGI wouldn’t just generate content or follow instructions, it could reason, adapt, and solve problems independently.

Why it matters:
AGI gets mentioned a lot in headlines and tech forecasts. While tools like ChatGPT, image generators, or automations may seem super smart, they are not AGI. They’re systems trained to follow patterns, not think freely.

⚠️ Consider this:
Vendors sometimes use “AGI” language to oversell what their tool can do. If a tool promises human-level thinking, judgment, or creativity without guardrails, read the fine print, and manage your expectations.

AI Agent

What is it:
A digital "team member" that can not only take a task, but figure out what task to do next. An AI agent is a system designed to complete multi-step goals with minimal human input. It can make decisions, use tools, and sometimes even interact with other software to move a task forward.

Why it matters:
AI agents are starting to show up in CRMs, scheduling tools, and customer service platforms. You might not build one from scratch, but you’ll likely use tools that embed them. Understanding how they operate helps you evaluate which tasks to delegate and which ones still need your judgment.

⚠️ Be careful:
AI agents work best in well-scoped environments. If your systems are fragile or your team is already stretched thin, agents can create confusion by moving too quickly or without oversight. Always test in low-risk areas first and build in human checkpoints.

AI Assistant

What is it:
A digital tool that helps you complete tasks by responding to prompts or automating simple workflows. It might answer questions, write content, summarize documents, schedule meetings, or support customer service. Some AI assistants are general-purpose (like ChatGPT); others are built into tools you already use.

Why it matters:
AI assistants can take repetitive work off your plate, especially when paired with clear systems. They're not employees or experts, but they can help you move faster when you know what to ask for.

⚠️ Be Careful:
AI assistants follow instructions, not judgment. If your prompts are unclear (or your expectations are too high) they’ll give you something that sounds helpful but might miss the mark. Think of them more like fast interns than flawless strategists.

AI Automation

What is it:
The use of AI tools to complete tasks without manual input—usually based on patterns, predictions, or rules. This might look like drafting emails, tagging leads, sending reminders, or updating a CRM based on behavior.

Why it matters:
AI automation can help reduce repetitive work and speed up simple decisions. It’s especially useful for handling tasks that follow clear patterns—like filtering inquiries or summarizing meeting notes. When set up well, it creates more breathing room for creative or client-facing work.

⚠️ Be careful:
Automating a broken process just makes the mess faster. Make sure the task itself makes sense, the logic is clear, and there’s a review step if needed—especially before it touches your clients or your brand voice.

AI Ethics

What is it:
The principles and questions that shape how AI should (or shouldn’t) be used. It includes things like data privacy, consent, transparency, fairness, accountability, and the broader impact of automation on people and systems.

Why it matters:
Even if you're not building your own AI, you're using tools that make ethical choices behind the scenes—what data they use, how they represent people, and what kind of labor they're replacing. Understanding basic AI ethics helps you make informed decisions about what you automate, how you use client data, and what standards you want your business to uphold.

⚠️ Be careful:
Not every tool is designed with your values in mind. Some AI platforms scrape data without consent, reinforce bias, or leave you liable for inaccuracies. If a tool feels “too easy,” take a second look—ethics often show up in the fine print.

AI Literacy

What is it:
Knowing how AI tools work, what they’re good at, and where they fall short. At its core, AI literacy is about being able to evaluate, use, and clearly talk about AI in your business.

Why it matters:
AI is showing up in everything from CRMs to scheduling tools. The more fluent you are with the basics, the easier it is to make good decisions, train your team, and avoid hype or false promises.

⚠️ Be careful:
Being "AI literate" doesn’t mean becoming an expert. But it does mean asking better questions about things like data use, brand voice, automation risks, and vendor claims.

AI Policy

What is it:
A set of internal guidelines that explain how your business uses AI tools—what’s allowed, what’s off-limits, and how things like client data, attribution, review, or brand voice are handled. It can be written for staff, contractors, or clients.

Why it matters:
If you’re using AI to speed up content, draft client messages, or automate parts of your workflow, having a clear policy protects you and your team avoid risky shortcuts. It sets expectations for quality, transparency, and boundaries.

⚠️ Be careful:
Without a policy, AI use can easily drift into gray areas, like using tools to write copy you can’t stand behind, or storing sensitive client info in platforms that aren't secure. Put something in writing before delegating AI-powered tasks.

AI Safety

What is it:
The practice of making sure AI systems behave in ways that are reliable, ethical, and aligned with human goals. That includes preventing harmful outputs, avoiding bias, protecting data, and setting clear limits on what a system can do.

Why it matters:
Even small AI tools can produce unexpected results, like hallucinated answers, tone mismatches, or data exposure. AI safety helps you manage those risks before they affect your brand, your clients, or your decision-making.

⚠️ Be careful:
Safety doesn’t come from the tool alone. How tools are used are equally important. Review steps, clear prompts, and internal policies are just as important as model quality.

AI Slop

What is it:
Low-effort, low-quality content churned out by AI tools and published without editing. It might sound okay at first glance, but it’s usually generic, repetitive, or off-brand.

Why it matters:
AI slop is everywhere right now. It floods inboxes, fills websites, and clutters search results. Publishing too much of it can quietly damage trust, confuse your audience, or dilute your brand voice.

⚠️ Be careful:
If your team or VA is churning out high volumes of content with little oversight, you're probably spreading slop...even if it looks “done.”

If you or your team are using AI to create content, make sure someone is reviewing and refining it—ideally with your brand voice and goals in mind.

 

Algorithm

What is it:
A set of rules or steps a computer follows to solve a problem or make a decision. Algorithms are behind everything from what posts show up in your feed to how an AI tool structures its response.

Why it matters:
Understanding that algorithms aren’t neutral helps explain why certain content gets seen, why AI makes the suggestions it does, or why two people might get very different results from the same tool.

⚠️ Be careful:
Algorithms reflect the values, data, and priorities of whoever built them. If something feels off (like odd ad targeting or repeated content suggestions) it’s worth asking what the system was optimized for.

 

Alignment

What is it:
Alignment is how well an AI system’s behavior matches human intentions. In plain terms, it’s whether the AI “does what you meant,” not just what you said. A well-aligned model takes into account your goals, values, and context, and not just the literal prompt. Think of it as the difference between a helpful teammate and a clever-but-clueless intern.

Why it matters:
As AI tools get more powerful, alignment becomes more important, especially in client-facing work. An AI that spits out content quickly isn’t helpful if it gets the tone wrong, makes assumptions, or creates something that subtly undermines your brand. Tools trained with strong alignment are more likely to give usable results, but they still need human oversight.

⚠️ Be careful:
Some tools claim to be “aligned with human values,” but whose values are they using? Alignment isn’t universal. It depends on culture, context, and use cases. What works for group, culture, brand or business could be off-base for another. Always test AI outputs through your own lens, not just the tool’s default.

API (Application Programming Interface)

What is it:
A behind-the-scenes connection that lets one tool share information with another. Think of it like a courier that passes data from one system to the next automatically.

Why it matters:
APIs are what let your tools “talk” to each other. If your form sends contacts to your CRM, or your AI tool pulls in data from a spreadsheet, that’s probably an API making it happen. While serious coding skills aren't required, it's helpful to be comfortable with technology, or hiring someone who can help set up and monitor API connections.

⚠️ Be careful:
APIs don’t always announce when they stop working. If an automation silently fails, you could lose data or send the wrong message. Keep a list of what’s connected, and check your systems if something feels off.

Artificial Intelligence (AI)

What is it:
Software that mimics human thinking to perform tasks like writing, answering questions, identifying patterns, or making recommendations. Not magic. It can be powerful if used strategically.

Why it matters:
AI tools are now built into everything from CRMs to ad platforms. Understanding how they work can help you assess whether a tool is actually saving time, supporting decisions, or introducing new complexity.

⚠️ Be careful:
AI isn’t magic. If you’re relying on AI to “fix” messy marketing, it will likely make the mess worse.  if you already have structure, process, and clear goals, AI can help you move faster and reduce repetitive work.

Agent Teams

What is it:
Think of this as a group of AI agents working together. Each one is responsible for a different step in a larger process. Just like a human team, they divide tasks, communicate with each other (via code), and collaborate to reach a shared goal. One agent might research, another might summarize, and a third might send the result to your CRM.

Why it matters:
These setups are starting to show up in more advanced tools, especially those promising “end-to-end” automation. It sounds great, but there’s often a lot going on behind the scenes. Knowing how the pieces fit together helps you spot when something’s off or out of sync.

⚠️ Be careful:
If one part of the system goes sideways, the whole thing can quietly unravel. Agent teams work best when every step is predictable and checkable. Start small, and make sure someone on your team knows what to look for if things break.

Bias

What is it:
When an AI system consistently favors one perspective or outcome over others, often unintentionally. This usually comes from the training data. If most examples come from a particular culture, region, or demographic, the AI may struggle with anything outside that frame.

Why it matters:
Bias affects how AI writes, what it assumes, and how it interprets your instructions. Even neutral-looking responses can reflect narrow defaults. Awareness of bias helps you catch tone issues, avoid inaccurate assumptions, and maintain trust in your content.

⚠️ Be careful:
Bias isn’t always obvious. It may show up as off-brand language, vague messaging, or copy that unintentionally excludes. You don’t have to rewrite everything, but it’s worth building in a review step, especially for public-facing or identity-sensitive content.

Chatbot

What is it:
A tool that lets people interact with your business through a text-based interface, usually on your website or inside an app. Some are simple (“click here for hours”), while others use AI to respond in full sentences, answer questions, or direct people to the right info.

Why it matters:
Chatbots can help filter leads, answer FAQs, and take pressure off your inbox or sales team. But they only work well when they’re set up with clear goals, guardrails, and language that reflects how your business actually talks.

⚠️ Be careful:
A chatbot that sounds robotic, confusing, or overly confident can frustrate visitors or send the wrong message. Don’t let it pretend to be you unless you’ve tested every response path.

CISCO Prompt

What is it:
A structured prompt format developed by Mountainside Media to help you get better results from ChatGPT. It stands for:

  • Context – What the AI needs to know
  • Intent – What you want to accomplish
  • Style – Tone, voice, or format to match
  • Commands – Specific instructions or tasks
  • Output – The format or structure you want in return

Why it matters:
Most AI issues come from unclear or incomplete instructions. The CISCO format gives your request structure so you don’t have to keep correcting or rewording the output. It works especially well for client-facing content, workflows, and brand messaging.

⚠️ Be careful:
CISCO helps you think clearly before you prompt, but if your instructions are vague, your output will be too, no matter how structured the format. For the best results, slow down and get specific first.

Cognitive Computing

What is it:
A design approach that tries to make AI systems more like human decision-makers. Instead of just generating content or making predictions, cognitive computing focuses on mimicking how people reason, learn from experience, and solve problems in real-world contexts.

Why it matters:
Cognitive computing allows humans to partner with AI tools to create systems that support complex, context-rich decisions. Think of tools that can help evaluate ambiguous scenarios, weigh conflicting data, or adapt as new information comes in. While you won’t see “cognitive computing” as a button in your CRM, the concept influences how AI is being developed for industries that rely on judgment—not just automation.

⚠️ Be careful:
Vendors may use “cognitive” as a buzzword to imply intelligence or nuance that isn’t really there. Ask what the tool actually does, how it handles uncertainty, and what human oversight is still required. Real cognition still lives in your head, not in the app.

Context Window

What is it:
The amount of information an AI model can “keep in mind” while it’s responding. Think of it like the AI’s short-term memory. If you go over the limit, it starts forgetting what came earlier.

Why it matters:
If you’re writing something long, giving detailed instructions, or referencing earlier messages, the size of the context window affects how well the AI can stay on track. A small window means you’ll need to repeat yourself more. A larger one gives you more room to think out loud.

⚠️ Be careful:
The AI won’t always warn you when it forgets something, it just starts making assumptions or hallucinating. For layered or multi-step tasks, double-check that your earlier inputs are still being followed.

Data Privacy

What is it:
Data privacy is about controlling who has access to your data, how it’s used, and whether it’s being stored, shared, or trained on without your permission. In AI tools, this can mean anything from protecting customer names and emails to ensuring your business data doesn’t get folded into someone else’s model.

Why it matters:
Many AI tools process sensitive information—client conversations, sales numbers, internal workflows. If you don’t know where that data is going, you risk exposing things you didn’t mean to share. Even tools that feel “private” may still store or use your data to improve their models unless you opt out or pay for a more secure tier.

⚠️ Be careful:
“Private” doesn’t always mean what you think. Always check the fine print. If you’re entering client data into a free tool, make sure it’s not being saved or used to train future versions. When in doubt, anonymize or redact sensitive details—or use tools that guarantee no data retention.

Discrimination

What is it:
In AI, discrimination refers to unfair or unequal treatment of people based on biased data or flawed algorithms. It can show up in subtle ways—like certain audiences not seeing your ads, or your chatbot responding differently to inquiries based on phrasing, names, or location. The system isn’t trying to be unfair—it’s just mirroring patterns in the data it was trained on.

Why it matters:
Even if you didn’t build the AI, you’re still responsible for how it behaves in your business. If your marketing, hiring, or customer support tools unintentionally treat some people differently, that can erode trust, damage your reputation, or even open you up to legal issues.

⚠️ Be careful:
Bias in = bias out. If your audience data isn’t diverse—or if the tools you’re using weren’t trained on inclusive data sets—discrimination can creep in quietly. Always test tools with a range of inputs, and watch for patterns that exclude or favor certain groups without a clear reason.

Fine-Tuning

What is it:
Customizing a model to speak your brand language, follow your SOPs, or handle specific workflows. It’s like giving the AI a focused bootcamp so it understands your tone, style, or internal logic better than a general model would.

Why it matters:
You’ll hear vendors talk about “training AI on your brand.” That might mean true fine-tuning—or just feeding examples into a prompt. The real thing can be powerful, especially for repeatable content or complex workflows—but it takes planning, structure, and clean inputs to do it well.

⚠️ Be careful:
Fine-tuning is easy to oversell. If the original model wasn’t a good fit, fine-tuning won’t fix that. And once it’s trained, it can’t unlearn bad inputs—so make sure you’re only reinforcing what actually works.

Generative AI

What is it:
AI that creates something new—like a blog post, image, summary, or email draft—based on patterns it’s seen during training. It’s not pulling from a database; it’s building fresh content from scratch (for better or worse).

Why it matters:
This is the kind of AI most people are using—whether it’s ChatGPT writing a caption or a design tool generating a graphic. It can speed up creative work, get you past blank-page moments, and help with brainstorming or repurposing.

⚠️ Be careful:
Just because it sounds fluent doesn’t mean it’s accurate—or on-brand. Generative AI will always fill in the blanks, even if it has no real context. Always review before using it externally.

GEO (Generative Engine Optimization)

What is it:
An emerging approach to SEO that focuses on making your content show up in AI-generated search results—not just traditional search rankings. Instead of optimizing for blue links on Google, GEO is about training AI engines to see your brand as a trusted source worth citing.

Why it matters:
As tools like ChatGPT, Google’s AI Overviews, and Perplexity replace traditional search boxes, visibility depends on whether these systems recognize and trust your content. GEO involves writing clearly, citing sources, maintaining topical authority, and showing up where your audience already is.

⚠️ Be careful:
This space is still evolving. Chasing GEO too aggressively can lead to shallow or overly structured content. Focus first on clarity, consistency, and source credibility—those are the traits AI engines are being trained to trust.

GPT (Generative Pre-trained Transformer)

What is it:
The architecture behind tools like ChatGPT. It's a type of large language model trained on massive amounts of text so it can predict and generate human-sounding language. “Generative” means it creates new content, “pre-trained” means it’s already learned from tons of examples, and “transformer” is just the technical name for how it handles sequences of words.

Why it matters:
You don’t need to memorize the acronym, but you will hear it often. GPT is what powers many AI tools used for writing, summarizing, drafting, or replying. Understanding that it’s predictive—not factual—helps explain why it sometimes sounds confident but gets things wrong.

⚠️ Be careful:
GPT isn’t searching the web. It’s predicting the next likely word based on patterns. That means it can sound convincing while delivering outdated, incomplete, or entirely made-up information.

Hallucinations

What is it:
A polite term for when AI makes something up. It might invent a statistic, cite a non-existent article, or confidently suggest an answer that isn’t true. It doesn’t know it’s wrong—it’s just filling in gaps based on patterns, not facts.

Why it matters:
Even when it sounds helpful, AI can introduce errors into your content, your process, or your decision-making. If it gives a number, quote, or reference, treat it like a rough draft until verified.

⚠️ Be careful:
Hallucinations aren’t always obvious. They often show up in small, plausible details—like an incorrect URL or a summary that “feels” right but isn’t. Don’t skip the human review, especially for anything public-facing or strategic.

Human in the Loop

What is it:
A setup where a person stays involved in the process—reviewing, correcting, or guiding the output of an AI system. Instead of letting the tool run on autopilot, a human steps in at key points to make decisions, catch errors, or add context.

Why it matters:
AI can move fast, but it doesn’t always move smart. Having a human in the loop helps catch tone issues, factual errors, or off-brand content before it reaches your audience. It also gives you more control over how automation affects your systems and relationships.

⚠️ Be careful:
“Human in the loop” doesn’t mean fixing things after they break. It means building in checkpoints before the output gets published, sent, or automated. The loop works best when it’s intentional—not reactive.

Large Language Model (LLM)

What is it:
A kind of AI trained on huge amounts of text so it can generate language that sounds natural. It learns by spotting patterns—how words and ideas tend to show up together—and then uses those patterns to respond to prompts.

Why it matters:
Most of the AI tools you’re using for content, research, or automation are powered by LLMs. They’re versatile and fast, but not magical. Their output depends entirely on how they’re prompted and what they’ve seen before.

⚠️ Be careful:
An LLM is skilled at sounding fluent—but it doesn’t know anything. Without clear prompts, examples, or limits, it might return something generic, vague, or off-brand. It’s a tool, not a source of truth.

Machine Learning (ML)

What is it:
A way for computers to recognize patterns and improve over time—without being explicitly programmed for every task. It’s the engine behind many AI tools, using past data to make decisions, categorize info, or refine performance.

Why it matters:
Machine learning powers everything from smart email filters to personalized recommendations. If an AI tool improves with use, it’s likely using ML behind the scenes.

⚠️ Be careful:
Just because something is “machine learning–powered” doesn’t mean it’s accurate, unbiased, or right for your needs. Good results depend on good data—and good context.

 

Markdown

What is it:
A simple way to add structure to plain text using symbols like *, #, and > to format things like headers, lists, or emphasis. It’s commonly used in tools like Notion, GitHub, or AI chat outputs.

Why it matters:
You might see Markdown show up in AI-generated content, especially if you're asking for bulleted lists, section titles, or styled outputs. Knowing what the symbols mean can help you clean things up or copy/paste with confidence.

⚠️ Be careful:
If Markdown tags look confusing or cluttered in the output, just delete them, or ask the AI to rewrite the response without formatting.

Model

What is it:
Think of a model as the “brain” behind a tool slike ChatGPT. It processes data and generates responses based on patterns it’s learned. Different models are better at different tasks. For example, Mountainside Media recommends Claude for writing, Chat GPT for data analysis and coding, Mid-Journey for Images, Google VEO for video, and Gemini for Agents.

Why it matters:
Most AI tools run on a specific model. Knowing which one powers the tool you’re using helps you understand what kinds of results you can expect, where it might stumble, and how much context it can retain.

⚠️ Be careful:
Not all models handle nuance equally. A model that’s great at casual writing might struggle with highly specific brand voice or strategic instructions unless clearly guided.

Multimodal AI

What is it:
AI that can handle more than just one type of input—like text, images, audio, or video. Instead of just reading or just looking, it can combine formats. For example, it might analyze a photo and describe it in writing, or read a chart and answer questions about it.

Why it matters:
This opens up new ways to use AI for content creation, accessibility, and automation. You might upload a screenshot, voice memo, or PDF and ask the AI to summarize, explain, or reformat it—without needing separate tools.

⚠️ Be careful:
Multimodal tools can misinterpret images or documents, especially if they’re blurry, complex, or formatted oddly. Always check the output before relying on it—especially if it’s meant for a client or public audience.

Natural Language Processing (NLP)

What is it:
The branch of AI that helps machines understand, interpret, and generate human language. It’s what makes tools like ChatGPT capable of holding conversations, summarizing notes, or editing your writing.

Why it matters:
NLP is what powers most of the AI tools you're using to work with text—emails, blog posts, captions, client messages. It’s also what makes AI feel “conversational” instead of robotic.

⚠️ Be careful:
NLP models are trained on common language patterns—not your brand’s nuance. If you're relying on them to speak for you, make sure you're giving them clear tone, context, and examples to work from.

Neural Network

What is it:
A type of computer system designed to loosely mimic how the human brain works. It processes information through layers—learning patterns and making decisions by strengthening connections over time.

Why it matters:
Neural networks are the backbone of modern AI, including tools like ChatGPT, image generators, and voice assistants. They’re what make the technology capable of learning from vast amounts of data and producing results that feel natural or creative.

⚠️ Be careful:
Neural networks are powerful—but also opaque. They don’t always reveal how they reached a decision. That makes them impressive, but also unpredictable. Use review steps and real-world checkpoints to keep things grounded.

Output

What is it:
The result you get back from an AI—whether it’s text, a list, a photo, a chart, or an email draft. It’s the end product of whatever prompt you gave.

Why it matters:
Outputs are only as good as the instructions they’re based on. If something feels off, it’s usually not the AI being “wrong”—it’s just answering a poorly scoped question or lacking context.

⚠️ Be careful:
It’s tempting to take the first draft and run with it. Don’t. Review for tone, clarity, accuracy, and fit—especially if the output will be seen by clients, customers, or your team.

Parameters

What is it:
Think of parameters as the internal settings or “dials” inside an AI model. They’re the millions (or billions) of little rules the model has learned about how language works—like what words tend to go together, how tone shifts in context, or what patterns signal a question. More parameters usually mean the model can handle more nuance, but it also needs more data and computing power to run.

Why it matters:
When AI companies brag about “175 billion parameters,” they’re pointing to how sophisticated their model is. But for small businesses, the number matters less than how the model behaves. Some high-parameter models write beautifully but require tighter prompting. Others are smaller but faster or more affordable to run. The real question is whether the tool works for your use case—not how many dials are under the hood.

⚠️ Be careful:
Don’t assume “more parameters” means better for you. A highly tuned model may sound smart but still give off-brand or unusable results if it’s not trained on the right data—or if your inputs aren’t clear. Always test before committing to a tool based on specs alone.

Predictive Analytics

What is it:
Using past data to make educated guesses about what’s likely to happen next. It doesn’t actually predict the future, it just spots patterns that can help you prepare for different possibilities and probable outcomes. For example, it might suggest when leads are most likely to convert or which campaigns drive the most traffic.

Why it matters:
Many CRMs, ad platforms, and dashboards now include AI-powered predictions, even if you don’t see the word “AI.” Understanding how these tools forecast trends can help you make more informed decisions, set realistic goals, or catch problems early.

⚠️ Be careful:
Predictive tools work best with clean, complete data. If your numbers are spotty or outdated, the forecasts will be too. Always pair predictions with real-world knowledge before making big changes.

Prompt

What is it:
The message, question, or instruction you give an AI tool to get it started. It can be as simple as “write an Instagram caption,” or as detailed as a multi-paragraph brand brief.

A good prompt makes the difference between a generic answer and a strategic, on-brand result. Think of it like giving your assistant context—not just barking commands.

Why it matters:
Prompts shape everything the AI gives back. The clearer the prompt, the more relevant and usable the output. If something feels off in the response, the first place to check is what you asked in the first place.

⚠️ Be careful:
Vague prompts usually lead to vague answers. If you want something specific—tone, format, voice—you have to ask for it. Treat prompting more like giving directions to a new team member, not a mind-reader.

Prompt Engineering

What is it:
The skill of crafting better prompts to get better results. This might mean giving structure, examples, constraints, or breaking complex tasks into smaller steps. You’re not coding—you’re guiding.

Why it matters:
Prompt engineering helps you get more consistent, usable output from AI tools. It can turn one-sentence requests into full workflows, and keep your brand voice, context, and goals intact across different types of content.

⚠️ Be careful:
You don’t need fancy jargon or formulas. Just clarity. If you find yourself fixing the same AI output over and over, it’s usually a prompt issue—not a model issue.

Prompt Formatting

What is it:
How you organize your prompt so the AI knows what to do. That might mean using bullet points, section headers, numbered instructions, or even using the words “Do this first, then that.”

Why it matters:
AI pays attention to structure. A messy prompt leads to messy results. If your request is clear on paper—with formatting, spacing, or sequence—the output will usually follow that shape.

⚠️ Be careful:
Formatting is part of the message. If you ask for a list but write a paragraph, you’ll confuse the AI. If you're trying to model a specific format, go beyond mere description, and show it in your prompt.

Prompt Library

What is it:
A collection of reusable AI prompts designed around your brand’s strategy, language, offers, and customer journey. These prompts help AI tools sound like you, follow your logic, and stay focused on what actually matters in your business.

Why it matters:
Prompt libraries reduce guesswork and save time. Instead of reinventing your message every time you open ChatGPT, you’re starting with tested, structured inputs that match your goals and voice. They’re especially helpful when delegating AI tasks to a team.

⚠️ Be careful:
A prompt library is only as good as the thinking behind it. If your tone, strategy, or offers aren’t clear, your prompts won’t work well, no matter how clever the wording.

Prompt Stacking

What is it:
Giving an AI a series of instructions, examples, or steps in one prompt to guide it through a more complex task. Think of it like layering context so the model understands what you want and how to deliver it.

Why it matters:
Stacking prompts helps you get better results, especially for longer-form content or tasks that need to follow a structure. It lets you keep everything in one conversation instead of starting from scratch each time.

⚠️ Be careful:
Too many layers without clear breaks can backfire. If the AI gets confused, split your request into smaller chunks or add formatting to guide it through.

Provider

What is it:
The company behind the AI model or tool you’re using. OpenAI (ChatGPT), Google (Gemini), Anthropic (Claude), and Meta (LLaMA) are examples. They build and maintain the infrastructure, training data, and rules that shape how the AI behaves.

Why it matters:
Each provider handles things a little differently. Some focus on privacy, others on speed, others on creative flexibility. Knowing who’s powering your tools helps you understand what to expect and where the data’s going.

⚠️ Be careful:
Some providers keep a copy of what you input. If you’re pasting in client work, private messages, or brand documents, check the provider’s data policy first. Not all tools treat your content as confidential by default.

RAG (Retrieval-Augmented Generation)

What is it:
A technique that combines a language model like ChatGPT with a custom source of information, like your website, PDFs, or knowledge base. Instead of just guessing based on training data, the AI can “look up” relevant info in real time before generating a response. It’s like giving your AI a mini library to search through before answering a question.

Why it matters:
RAG is what powers most custom chatbots and brand-trained AI tools. It gives you more control over what the AI references. That way, so answers are more accurate, on-brand, and grounded in your actual content, not just the internet. That means fewer hallucinations and more confidence that the output reflects your business.

⚠️ Be careful:
Just because an AI tool uses RAG doesn’t mean it’s reading your content correctly. If the source material is disorganized, outdated, or full of filler, your AI’s output will reflect that. Garbage in, garbage out still applies. RAG just moves the garbage into a prettier package.

Singularity

What is it:
A speculative point in the future where AI becomes so advanced that it outpaces human control or understanding—triggering rapid, irreversible change. It’s often tied to science fiction or philosophical debates about AI surpassing human intelligence.

Why it matters:
The Singularity is more of a thought experiment than a business concern. But you may hear it mentioned in big-picture AI conversations, often with dramatic predictions about jobs, society, or innovation.

⚠️ Be careful:
This term can create confusion or anxiety around what AI is doing now versus what people imagine it might do someday. If you're trying to make practical decisions for your business, you can safely ignore Singularity hype and focus on what current tools actually offer.

Servers

What is it:
High-powered machines (often located in data centers) that store, process, and deliver data and resources over the internet. When you use AI tools, much of the work isn’t happening on your laptop. It’s happening on these machines, which form the backbone of all cloud and AI services.

Why it matters:
AI workloads often run on specialized "AI servers" built for heavy computing tasks—whether training models or generating responses in real time. These systems differ from normal office servers: they use powerful GPUs, tailored cooling systems, and real-time energy management to handle the high demands supermicro.com. That affects performance, cost, speed, and even your tool’s carbon footprint.

⚠️ Be careful:
Most AI tools don’t run on their own servers, they call an API that taps into someone else’s. That means your prompts (and sometimes client data) may pass through third-party systems you don’t control. It also means your tool’s performance depends on how well those external servers handle the load. If a tool feels slow or unreliable, the issue may not be the software. It may be the infrastructure it's renting.

Temperature

What is it:
A setting that controls how creative or predictable an AI’s responses are. Lower temperatures (like 0.2) make the model more focused and consistent. It's ideal for things like technical writing or summaries. Higher temperatures (like 0.8 or 1.0) make it more creative and varied, which can help with brainstorming, but also increases the risk of it going off-track or “hallucinating.”

Why it matters:
Temperature helps you manage tone and reliability. If you're using AI to generate web copy, ad headlines, or client-facing materials, a lower temperature helps keep it on-message. If you're looking for creative directions or new ideas, turning up the temperature might spark something unexpected.

⚠️ Be careful:
Higher temperature doesn’t mean “better” ideas, just weirder ones. If your outputs feel too random or irrelevant, check the temperature setting. And remember: no setting can replace a clear prompt. Garbage in, garbage out.

Tokens

What is it:
Tokens are the “currency” of most AI models. They represent chunks of text. Think of them like word fragments. A single word might be one token or several, depending on how it’s broken down. You don’t need to count them manually, but be aware that AI tools often charge based on token usage. Longer tasks = more tokens = higher cost.

Why it matters:
Most AI tools charge or limit usage based on token count, not word count. Understanding what counts as a token helps you estimate cost and manage how much text you can include in one prompt or response.

⚠️ Be careful:
Token limits aren’t just about price, they affect how much the AI can “remember” in one response. If you’re working with long content or detailed tasks, hitting the token limit can cause it to cut off early or forget key instructions.

Tone of Voice (TOV)

What is it:
The consistent personality, phrasing style, and emotional tone your brand uses across content, emails, ads, and client communication. It’s not what you say. It’s how you say it. Some brands are warm and chatty, others are dry and direct. Your tone helps your audience recognize and trust you.

Why it matters:
AI tools will default to a bland, generic voice unless told otherwise. If you want consistent messaging (whether you’re writing a caption or drafting a nurture sequence) you’ll need to show the AI your tone and ask it to follow it.

⚠️ Be careful:
Tone isn’t something you can describe once and forget. It needs reinforcement through examples, edits, and checkpoints, especially if multiple people or tools are creating content on your behalf.

Training Data

What is it:
The information an AI learns from before it ever responds to you. This includes books, websites, articles, code, and all kinds of text-based content. Sometimes it can also include images or video, depending on the model.

Why it matters:
AI tools don’t “know” things in the way people do. They generate answers based on what they’ve seen during training. If something wasn’t included, or was misrepresented, the model won’t have much to work with.

⚠️ Be careful:
Just because AI says it with confidence doesn’t mean it’s accurate. If you work in a niche industry or have a distinct audience, assume the model hasn’t seen enough to speak fluently without guidance.

Transparency

What is it:
Transparency means being able to see and understand how an AI system works, including how it was trained, what data it uses, and why it made a particular decision or gave a certain answer. A transparent tool doesn’t feel like a black box. You can trace the logic behind its output, or at least know what influenced it.

Why it matters:
Transparency helps you trust what you’re using. If you don’t know how an AI came to a conclusion (or what it might be missing) it’s harder to rely on the results, especially in high-stakes work like marketing, analytics, or decision support. Transparent systems make it easier to spot gaps, explain results to clients, and improve accuracy over time.

⚠️ Be careful:
Many AI tools aren’t fully transparent, even when marketed as “open” or “ethical.” If a vendor can’t explain where their data came from or how their model was evaluated, be cautious. And if a tool can’t tell you why it gave a certain answer, don’t base critical decisions on it.

Vibe Coding

What is it:
Vibe coding is when you design or build based on how something feels rather than how it functions. It’s common in low-code tools, websites, and AI prompts where decisions are driven by aesthetic instinct instead of structured logic. You might skip wireframes and say things like, “Make it feel elevated,” or “It should look clean and modern,” without defining what that means or how it supports the user experience.

Why it matters:
This approach can move projects forward quickly, especially in early creative phases. But if everything is based on vibes, and there are no systems, constraints, or clarity, it can lead to designs that look good but don’t work well. Small businesses often default to vibe coding when they’re overwhelmed or chasing trends, but the results are hard to maintain, scale, or measure.

⚠️ Be careful:
Vibe coding isn’t wrong. It’s just incomplete. If you’re building a tool, website, or AI prompt meant to drive results, you need more than a mood. Use vibes to start the conversation, not finish the build. Strategy gives those instincts structure.

Word Salad

What is it:
AI-generated text that sounds polished but doesn’t actually say anything. It might be grammatically correct, well-structured, and even on-topic. But when you slow down and read it, it’s vague, repetitive, or just filling space without delivering insight.

Why it matters:
Word salad is common in AI outputs, especially when prompts are overly broad or generic. It can slip past review because it feels right on a quick skim.

⚠️ Be careful:
Word salad often hides in writing that feels complete but never makes a clear point. If something looks fine but leaves you wondering what it actually said, you’re probably looking at AI-generated filler.

References

Data-Driven Marketing Tips