The AI Glossary You Pretend You Don’t Need (But Definitely Do)
A clear, no-jargon glossary explaining essential AI concepts for teams, founders, and operators.
Here’s something we’ve noticed over the last year of working with teams, founders, and operators: when it comes to AI terminology, everyone knows about half of it… and the other half is new to everyone, including us.
It’s not a lack of intelligence. It’s simply that the field is evolving faster than the vocabulary around it. New terms keep appearing, old ones keep shifting, and the definitions you read online often feel like they were written by a committee of robots who’ve never spoken to a human.
So we put together this guide, a simple, conversational walk through the 101, 201, and 301-level concepts that are shaping how AI actually works today. No jargon. No mystique. Just the stuff that fills in the gaps so you can make better decisions and explain things without pulling up a 40-tab research session.
Think of it as a shared glossary for the rest of us; the people building, leading, experimenting, and occasionally pretending we already knew what “vectorization” meant.
If it helps you learn something new, great.
If it helps your team speak a common language, even better.
Let’s dive in.
101 LEVEL
1. Context Window
AI holds our words
Some fade, and some stay alive
Memory shapes truth
What it is
Here’s something interesting: every AI has a kind of “working memory.” A context window is simply how much information it can hold in its mind at once. The bigger the window, the more of your conversation, document, or dataset it can meaningfully stay aware of.
Real-world example
It’s like the difference between someone who remembers your last sentence… and someone who remembers your entire quarterly strategy conversation without losing the plot.
Why this matters for you
If you’re analyzing long transcripts, multi-chapter reports, dense briefs, or anything with moving pieces, a larger context window gives you clarity instead of fragmentation.
It’s the difference between AI that reacts… and AI that understands.
2. Tokens
Words split into parts
Small pieces count the whole cost
Length shapes what you get
What it is
AI doesn’t read whole words, it breaks them into pieces called tokens. Every input you send and every output you receive is counted in tokens.
Real-world example
“Playing” becomes “play” + “ing.”
“Internationalization” becomes multiple small pieces.
Why this matters for you
Costs, limits, and cutoffs depend on token usage.
Longer prompts = more tokens.
Longer answers = more tokens.
Exceed the token limit and the model simply can’t process your request.
If you’ve ever wondered why a long prompt gets clipped, tokens are the reason.
3. Fine-tuning
A broad mind refined
Learns the work of one true craft
Depth beats what is wide
What it is
Fine-tuning teaches a general AI to excel at a specialized job by training it on domain-specific examples. Think of it as giving a brilliant generalist a focused apprenticeship.
Real-world example
A medical system fine-tunes a model using tens of thousands of anonymized patient notes. Suddenly it understands clinical nuance that generic models miss.
Why this matters for you
You may never personally fine-tune a model but understanding it helps you evaluate tools.
A purpose-built AI trained on your niche will almost always outperform a general model.
This is how specialized AI products are created.
4. Latency
A quiet waiting
The pause before thought appears
Speed shapes how we feel
What it is
Latency is simply the pause between your question and the AI’s answer.
Real-world example
If you’ve ever watched an AI “think” for a few seconds before responding, that’s latency.
Why this matters for you
Low latency creates more natural interactions.
Critical for:
• voice assistants
• live translation
• co-working tools
• real-time analytics
When latency is high, the entire experience feels sluggish and people stop using the tool.
5. RLHF (Reinforcement Learning from Human Feedback)
We teach with our touch
A chorus carried through time
Light learns from our care
What it is
It’s how AI learns what “good” looks like. Humans give feedback on outputs, and the model adjusts its behavior over time.
Real-world example
Every 👍/👎 in ChatGPT directly trains future models.
It’s the world’s largest human feedback loop.
Why this matters for you
This is why AI systems start feeling more helpful over time, they’re shaped by millions of human corrections.
It also explains why models develop more aligned, less chaotic behaviors.
6. Retrieval / RAG (Retrieval-Augmented Generation)
It searches your world
Finds truth in the lines you keep
Answers grow from you
What it is
RAG allows AI to “look up” information in real time from your documents, wikis, Notion, databases, or the web.
Instead of relying on its training, it grounds its answer in your actual data.
Real-world example
A support chatbot that answers using your internal knowledge base not generic internet memory.
Why this matters for you
RAG is the backbone of any “AI over your data” system.
Good RAG reduces hallucinations.
Great RAG produces answers that look like they came from your best strategist.
Your data quality becomes your competitive advantage.
7. Prompt Patterns (Prompt Engineering)
A shape for the mind
Clear paths turn guesswork to craft
Order sparks good work
What it is
Patterns are reusable structures that shape how the AI should think: roles, formats, examples, guardrails, reasoning steps, and self-checks.
Real-world example
Instead of: “Write a sales email.”
You say:
“You are a B2B strategist. Use this structure: 1) personalized hook, 2) problem insight, 3) proof, 4) CTA. Write 3 variations for a VP of Marketing.”
Why this matters for you
Prompt patterns turn AI into an operational tool, not a novelty.
Teams get consistency.
Leaders get predictable output.
Workflows become repeatable.
And the model starts to feel like a system, not a slot machine.
201 LEVEL
8. Embeddings
Meaning turned to form
Distant words shown side by side
Thought finds its true shape
What it is
An embedding is a numerical representation of meaning a way for AI to measure how similar two ideas are.
Real-world example
To an embedding model:
“customer churn” and “users who stop renewing”
are nearly identical, even though the words differ.
Why this matters for you
Embeddings power:
• semantic search
• document matching
• clustering
• recommendations
• RAG accuracy
If you want AI to understand nuance and intent, embeddings are the engine.
9. Transformers
Little brain lights blink
Choosing words like picky cats
Meaning shuffles in
What it is
Transformers are the architecture behind almost every modern AI model. Their signature innovation is “attention,” the ability to focus on the most relevant parts of your input.
Real-world example
A transformer deciding which words shape meaning:
“She didn’t say he stole the money.”
Shift attention and the meaning changes completely.
Why this matters for you
Prompts work because transformers respond to structure and clarity.
If your input is focused and well-organized, the model’s attention becomes sharper and the output improves dramatically.
10. Multi-modal Models
Streams of sense converge
One mind listens to them all
A quiet knowing
What it is
These models can process more than one type of input at the same time; text, images, audio, video, code, and sometimes even data tables.
Real-world example
Upload a screenshot → Ask “What’s confusing here?” → Get a UX breakdown.
Or record an audio note → Get a crisp strategy summary.
Why this matters for you
This is where AI becomes a true collaborator.
You’re no longer limited to text.
It can sit inside your workflows; product, marketing, analysis, creative and move fluidly between formats.
11. Guardrails & Alignment
Stay in your lane please
Silly bot mind your manners
Good choice keeps us safe
What it is
Guardrails set boundaries for what an AI can say. Alignment is the process of making the model’s decisions match human values, brand tone, and ethical expectations.
Real-world example
When a model refuses harmful advice or defaults to a neutral, professional tone that’s alignment.
Why this matters for you
In organizations, guardrails aren’t optional. They protect brand voice, reduce compliance risk, and ensure AI supports your standards instead of undermining them.
12. System Instructions (Fine-Grained Control)
Rules for robot brains
Teach it how to think each time
One voice everywhere
What it is
This is the deeper layer beneath prompts, the foundational behaviors that govern tone, priorities, and decision logic.
Real-world example
A company sets rules like:
“Be calm, precise, and neutral. Cite uncertainty. Prioritize accuracy over creativity.”
Every output suddenly feels consistent across tools, teams, and contexts.
Why this matters for you
System instructions are how organizations scale AI.
Prompts shape one answer.
System instructions shape every answer.
13. Vector Databases
Strolling in bright light
Meaning blooms behind each door
Truth finds you with ease
What it is
A vector database stores embeddings and allows the AI to search by meaning, not keywords.
Real-world example
You search “pricing confusion” and it returns the exact slide where you explained your value ladder; even if you never used the phrase “pricing confusion.”
Why this matters for you
Vector databases are critical for:
• high-quality retrieval
• consistent RAG results
• knowledge management
• large-scale organizational AI
This is the difference between “it guesses” and “it actually understands your library.”
301 LEVEL
14. Distillation (Model Compression)
Big mind teaches small
A bright stroll to something new
Light steps carry more
What it is
Distillation is the process of taking a large, extremely capable AI model and training a smaller model to mimic its behavior.
Think of it as compressing intelligence not by shrinking the ideas, but by transferring the essential patterns.
Real-world example
A giant foundation model with hundreds of billions of parameters “teaches” a leaner version how to answer the same way.
The result?
A model that performs close to the original but is faster, cheaper, and easier to deploy on real-world hardware.
Why this matters for you
This is how enterprise AI becomes practical:
• on-device AI for privacy-sensitive environments
• lightweight models embedded inside products
• reduced latency for real-time workflows
• cost-effective scaling across teams and tools
Distillation is the bridge between “AI research” and “AI in production.”
It’s how you bring intelligence closer to the problem instead of sending everything to the cloud.
15. Agentic Workflows (Autonomous AI Agents)
Little bot gone wild
Runs the whole place by itself
I just ate my lunch
What it is
Agents are AI systems that can take multi-step actions toward a goal; planning, deciding, executing, and adjusting without waiting for human prompts.
They don’t just answer a question.
They run a process.
Real-world example
You give an agent a goal like:
“Analyze our competitors, map their messaging shifts, and produce a summary with recommended opportunities.”
The agent then:
• searches the web
• reads reports
• extracts trends
• organizes insights
• drafts a deck
• checks its work
• loops until the quality is high
No supervision.
Just direction + guardrails.
Why this matters for you
This is where AI moves from assistant → teammate → operator.
Agents can eventually:
• run weekly marketing intelligence scans
• monitor brand sentiment
• audit SEO shifts daily
• generate report drafts
• identify anomalies
• notify you before problems escalate
This is the early foundation of AI as an internal “team member” that works 24/7 without fatigue.
The future org chart includes agents.
Ryan Edwards, CAMINO5 | Co-Founder
Ryan Edwards is the Co-Founder and Head of Strategy at CAMINO5, a consultancy focused on digital strategy and consumer journey design. With over 25 years of experience across brand, tech, and marketing innovation, he’s led initiatives for Fortune 500s including Oracle, NBCUniversal, Sony, Disney, and Kaiser Permanente.
Ryan’s work spans brand repositioning, AI-integrated workflows, and full-funnel strategy. He helps companies cut through complexity, regain clarity, and build for what’s next.
Connect on LinkedIn: ryanedwards2