Part 1: The 5 foundation concepts
📌 This is Part 1 of 5 in the AI Terms series. Start here before touching any AI tool.
📖 Explore more on Marketing, Media & AI: all articles at tommyacademy.io/articles
“You do not need to build AI. You need to speak its language to buy-it, brief-it, and evaluate-it.” (Tommy Nguyen)
1️⃣ Generative AI (GenAI)
📌 Definition: AI systems that create new content: text, images, video, code, music rather than just analyzing or classifying existing data.
Why it matters for marketers: GenAI is behind ChatGPT, Claude, Gemini, Midjourney, and DALL-E.
It creates first drafts, ad copy, social posts, and visuals.
It does not replace your creative team.
It gives them a starting point.
Watch out: GenAI outputs look confident but are not always accurate.
Always verify claims, data, and brand voice before publishing.
Check the AI risks: https://tommyacademy.io/2026/03/18/ai-risks/
2️⃣ Large Language Model (LLM)
📌 Definition: A type of AI model trained on massive amounts of text data to understand and generate human language.
“Large” refers to billions of parameters: the internal settings the model learns during training.
Why it matters for marketers: LLMs power ChatGPT (GPT-4), Claude (Anthropic), and Gemini (Google).
When you use these tools for content, research, or analysis, you are interacting with an LLM.
Watch out: LLMs predict the most likely next word.
They do not “understand” or “know” things.
They generate plausible text, not verified truth.
3️⃣ Natural Language Processing (NLP)
📌 Definition: The branch of AI that deals with how machines understand, interpret, and generate human language.
NLP includes tasks like sentiment analysis, translation, text classification, and chatbots.
Why it matters for marketers: NLP powers social listening tools (Brandwatch, Sprinklr), chatbots, email categorization, and review analysis.
Every time you analyze customer sentiment at scale, NLP is doing the work.
Watch out: NLP struggles with sarcasm, slang, code-switching (common in Vietnamese-English marketing), and context-dependent meaning.
4️⃣ Prompt
📌 Definition: The instruction or question you give to an AI model.
The quality of the output depends heavily on the quality of the prompt.
“Prompt engineering” is the skill of crafting effective instructions.
Why it matters for marketers: The same AI tool produces dramatically different results depending on how you prompt it.
A vague prompt gets a generic answer.
A specific prompt with context, constraints, and examples gets professional-grade output.
Practical tip: Include role (“You are a senior media planner”), context (“for a FMCG brand in Vietnam”), format (“in a table with 5 columns”), and constraints (“under 300 words”) in every prompt.
5️⃣ Token
📌 Definition: The basic unit of text that AI models process.
One token is roughly 4 characters or 0.75 words in English.
AI models have token limits — the maximum amount of text they can process in one conversation.
Why it matters for marketers: Token limits determine how much context you can give an AI and how long its responses can be.
GPT-4 handles ~128K tokens. Claude handles ~200K tokens.
If your brief or document exceeds the limit, the AI literally cannot read all of it.
Practical tip: When working with long documents, prioritize what the AI sees first.
Put the most important context at the top of your prompt.
💡 You do not need to become an AI engineer. You need to know enough to ask the right questions, set the right expectations, and catch the wrong outputs.
👉 Next: Part 2 covers Model & Training terms: Fine-tuning, RAG, Hallucination, Parameters, and Training Data.
See more about marketing & media: https://tommyacademy.io/publication/
❓ Frequently Asked Questions
What is the difference between Generative AI and regular AI?
Regular AI analyzes, classifies, or predicts based on existing data.
Generative AI creates new content: text, images, code, music that did not exist before.
ChatGPT and Midjourney are generative; Google Analytics and recommendation engines are not.
What is a token in AI and why does it matter?
A token is the basic unit of text that AI models process, roughly equal to 4 characters or 0.75 words in English.
Token limits determine how much information an AI can handle in one conversation.
Exceeding the limit means the AI cannot read your full input.
Do marketers need to learn prompt engineering?
Yes. The quality of AI output depends directly on the quality of the prompt.
Learning to write specific, structured prompts with role, context, format, and constraints dramatically improves results without requiring any technical AI knowledge.
🤝 Need clarity in Marketing, Media & AI? Reach out on LinkedIn or email TommyAcademy.vn@gmail.com to start a conversation.
P/s: Opinions are my own. Please take consideration for your actions.
This is as for informational & educational purpose, No liability for actions taken.
Nothing in this article constitutes legal, compliance, or regulatory advice.
© 2026 TommyAcademy. All rights reserved.
Leave a Reply