AI Without Jargon.

The only 10 words you need.

You don't need a computer science degree to use AI in your business. You do need to understand about 10 words — because vendors, consultants, and your tech-savvy employees will throw them at you constantly. These are the only ones that matter. Flip each card to get the plain-English version.

A note on how these are organized: each term includes a plain-English definition AND a business analogy. The analogy is the part that sticks. When someone says "tokens" in a meeting, your brain should immediately think "AI billing meter" — not the technical definition. That shortcut is what makes you fluent instead of just informed.

Do not try to memorize all 10 at once. Read through them once now. Then come back to this section whenever you encounter a term in the real world. After 2-3 real encounters, the definition will stick permanently. That is how language acquisition works — exposure in context beats memorization every time.

Each definition below also includes a practical note about why the term matters to your bottom line. Understanding what a token is matters because tokens determine your AI costs. Understanding what a hallucination is matters because hallucinations determine your risk exposure. Every term connects to money, risk, or productivity.

One more tip: as you learn these terms, start using them in conversations with your team and your vendors. Using the vocabulary — not just recognizing it — is what builds real fluency. When you say "what is our token cost per month?" in a meeting, you signal competence. When you say "has this output been checked for hallucinations?" you signal rigor. Language shapes how people perceive your AI expertise, even before you have years of experience.

The 10 terms below are organized by how frequently you will encounter them. The first five — RAG, Multimodal, Context Window, Temperature, and Agent — show up almost daily once you start using AI tools. The last five — Guardrails, Latency, Workflow Automation, Synthetic Data, and Edge AI — show up in deeper technical conversations: vendor evaluations, security reviews, and integration planning.

Learn the first five now. Come back for the last five when you need them.

And remember: the goal is not to use these terms to impress people. The goal is to understand what you are buying, what you are paying for, and whether the capability behind the jargon actually solves your business problem. Fluency serves decision-making, not ego.

For each term below, the back of the card explains what it means in business terms — not computer science terms. If you want the technical deep dive, there are excellent resources for that.

This course is about making better business decisions, not passing an engineering exam.

Every definition is written so you can use it in a conversation with your CFO, your client, or your board — people who care about outcomes, not architecture.

How these terms show up in real conversations.

Knowing the definitions is step one. The real value is recognizing these terms in context — when a vendor pitches you, when your developer mentions them in a meeting, or when you're reading an AI product page and trying to figure out what it actually does.

Here are five real scenarios you will encounter in your first month with AI tools:

Scenario 1: The vendor pitch. "Our platform uses a fine-tuned LLM with RAG capabilities." Translation: they took an existing AI model, trained it on industry-specific data, and added the ability to search your documents before answering. This is good — it means their answers will be grounded in your data, not just general knowledge.

Scenario 2: The cost conversation. "We charge $0.01 per 1,000 input tokens and $0.03 per 1,000 output tokens." Translation: they charge by how much text you send in (your prompt) and how much text the AI sends back (the response). A 500-word prompt costs about $0.004. A 1,000-word response costs about $0.02. Your monthly bill depends on volume.

Scenario 3: The accuracy concern. "We've seen some hallucinations in the output." Translation: the AI made things up. This is normal and expected. The fix is verification, not abandoning AI. Build a review step into your workflow — the same way you'd proofread a junior employee's first draft.

Scenario 4: The integration question. "Can we connect this to our CRM via API?" Translation: can the AI tool talk to your customer database automatically? If yes, your team stops copy-pasting between systems. If no, someone is still doing manual data entry.

Scenario 5: The capability check. "This uses generative AI, not just search." Translation: it creates new content rather than finding existing content. A search engine finds pages. Generative AI writes the page. Big difference when you're evaluating tools for content production, proposal writing, or customer communication.

Notice the pattern in these scenarios: every vendor conversation contains 2-3 jargon terms that sound impressive but translate to simple concepts. The skill is not knowing every term in advance — it is developing the reflex to pause and translate before agreeing to anything. If you cannot explain what a vendor just said in one sentence to a non-technical colleague, you do not understand it well enough to buy it.

One more scenario to cement this pattern:

Bonus scenario: The investor update. "We are leveraging generative AI and embeddings to deliver inference at the edge with sub-100ms latency." Translation: we use AI to create content and understand meaning, and we run it on local devices (not in the cloud) so it responds in under a tenth of a second. This sounds impressive — and it is technically real — but the business question is: "What does this mean for my user experience?" If the answer is "faster responses," say that. If the answer is unclear, ask.

Your jargon-busting prompt template.

When you encounter AI jargon you don't understand — in a pitch deck, a product page, a Slack message from your dev team — use this prompt to get a plain-English translation instantly. This is possibly the most useful prompt in the entire course because it turns any future jargon encounter into a 30-second translation exercise instead of a 30-minute research session.

Save this prompt in your prompt library (which you will build in Lesson 7). You will use it more often than you expect — especially in your first 90 days when every vendor meeting, product page, and technical conversation throws new terms at you. The prompt does not just define terms. It tells you whether the term represents a real capability you should care about or marketing language designed to impress without informing.

Here is a pro tip: when you get the translation back, forward it to your team. Building shared vocabulary across your organization prevents the situation where only one person understands what the vendor is selling. Shared understanding leads to better decisions.

This single prompt replaces hours of Googling. Use it every time you hit a wall of technical language. Over time, you'll internalize the vocabulary — but until then, let AI translate AI for you.

10 more terms you will encounter.

The first 10 terms get you through conversations. These next 10 show up when you start reading product pages, evaluating vendors, and sitting in technical meetings. You do not need to memorize them all at once — but you need to recognize them so you are never caught off guard.

These terms tend to appear in more advanced contexts: vendor contracts, technical proposals, product comparison pages, and security reviews. When you see them, you will know exactly what they mean and whether they matter for your business. As with the first 10, each card includes a business-relevant explanation — not just the technical definition. The technical definition tells you what the term means. The business explanation tells you why you should care and how it affects your budget, your team, or your customers.

The jargon red flag guide.

Not all jargon is honest. Some terms are used precisely because they sound impressive while meaning very little. Here is how to tell the difference between substance and smoke.

Red flag 1: "Proprietary AI." Almost every AI company uses the same handful of foundation models (GPT, Claude, Llama, Gemini) underneath. When a vendor says "proprietary AI" without specifics, ask: "Is this a model you trained from scratch, or a fine-tuned version of an existing model?" The answer tells you whether they built something genuinely new or wrapped a standard model in their branding.

Red flag 2: "AI-powered." This phrase is attached to everything from toothbrushes to accounting software. It has become meaningless on its own. Ask: "What specifically does the AI do in your product?" If they cannot name the exact function — summarizing, generating, classifying, predicting — the AI is likely a minor feature, not the core product.

Red flag 3: "Enterprise-grade AI." This usually means "expensive." It may also mean the product has genuine security, compliance, and scalability features — but the term itself is marketing. Ask for the specifics: SOC 2 certification, data residency options, SLA commitments, audit logs. Those are real enterprise features. The label alone is not.

Red flag 4: "Self-learning AI." All AI learns during training. Very few AI products genuinely learn from your usage in real time. When a vendor says self-learning, ask: "Does it improve from my specific data, or does it improve from aggregated user data across all customers?" The difference matters for both performance and privacy.

Red flag 5: "Human-level AI." No current AI system matches human judgment across all tasks. AI exceeds human performance on specific narrow tasks (pattern matching in large datasets, translation speed, first-draft writing speed) but falls short on others (nuance, ethics, creativity, relationship-building). Any claim of "human-level" should specify the exact task being compared.

Five more real conversations decoded.

Now that you have 20 terms and know the red flags, here are five more real-world scenarios where jargon shows up — this time with the advanced terms included. Practice reading these until the translation happens automatically.

Scenario 6: The product demo. "Our platform uses multimodal AI with a 128K context window, so your team can upload contracts, images, and spreadsheets in a single conversation." Translation: this tool processes text, images, and files together (multimodal), and it can handle very long documents without forgetting the beginning (128K context window). This is a real capability that matters if you work with long documents. Ask: "What happens when a document exceeds the context window?"

Scenario 7: The technical proposal. "We recommend deploying an AI agent that monitors your inbox, drafts responses, and routes exceptions to your team." Translation: they want to set up AI that acts on its own (agent) — reading emails, writing replies, and only involving humans when something unusual comes up. This is more advanced than a chatbot. Ask: "What guardrails prevent the agent from sending a response I would not approve?"

Scenario 8: The privacy conversation. "We use RAG with on-premise deployment so your data never leaves your network." Translation: the AI searches your documents before answering (RAG) and runs on your own servers, not in the cloud (on-premise). This is the gold standard for data-sensitive businesses. Ask: "What is the setup cost and ongoing maintenance for on-premise?"

Scenario 9: The budget meeting. "At current usage, latency is under 2 seconds. If we scale to 10x traffic, we will need edge deployment to maintain response times." Translation: responses are fast now (low latency), but if usage grows significantly, they will need to run the AI closer to users' devices (edge AI) to keep it fast. Ask: "What does edge deployment add to the monthly cost?"

Scenario 10: The compliance review. "We tested the model using synthetic data to verify it handles PII correctly without exposing real customer records." Translation: they created fake but realistic data (synthetic data) to test the system instead of using real customer data. This is good practice — it means they take privacy seriously during development. Ask: "How often do you re-test with updated synthetic datasets?"

Quick-reference glossary.

Bookmark this section. When you hit an unfamiliar term in a meeting, pitch deck, or product page, check here first. These are the 20 terms from this lesson in alphabetical order with one-line definitions.

Quick check.

Five questions. Business-context scenarios. This is not a memory test — it is a comprehension check to make sure you can apply these terms in real situations, not just recite definitions.

A note before you start: these questions use terms from both the essential 10 and the advanced 10. If a term feels unfamiliar, scroll up and review the relevant flashcard. Understanding these terms in context — not just as isolated definitions — is the skill that separates business owners who get bamboozled by AI vendors from those who ask the right questions and make confident decisions.

Each question presents a scenario you will encounter in your first 90 days of AI adoption. The correct answer tests whether you understand what the terms mean in practice, not whether you memorized a flashcard. Take your time.

When you finish: screenshot your score and save it. In 90 days, after you have been using AI tools daily, take this quiz again. Your score will improve because you will have lived these concepts, not just studied them. The gap between your first and second score is a measure of real learning.

If you score below 60%: do not move on yet. Re-read Sections 1-5, paying attention to the scenarios and red flags. The terms in this lesson are the vocabulary you will use for the rest of the course. Every subsequent lesson assumes you can read AI jargon fluently. Build that fluency here.