Part 2: How AI models learn & where they fail
📌 This is Part 2 AI Terms Every Marketer Must Know series, including Fine-tuning, RAG, Hallucination, Parameters, Training Data.
These terms explain why AI sometimes gives brilliant answers and sometimes confidently makes things up.
📖 Explore more on Marketing, Media & AI: all articles at tommyacademy.io/articles
“AI does not lie, it just hallucinates. Importantly, hallucinations delivered with confidence are more dangerous than silence.” (Tommy Nguyen)
6️⃣ Fine-tuning
📌 Definition: The process of taking a pre-trained AI model and training it further on a specific dataset to specialize its performance for a particular task or domain.
Why it matters for marketers: A generic LLM writes generic content.
A fine-tuned model trained on your brand voice, past campaigns, & industry data writes content that sounds like you.
This is the difference between “AI-assisted” and “AI that actually understands your brand.”
Watch out: Fine-tuning requires clean, representative data.
Bad training data creates a model that is confidently wrong in your brand’s voice. 😅
7️⃣ RAG (Retrieval-Augmented Generation)
📌 Definition: A technique where the AI retrieves relevant information from an external knowledge base before generating a response.
Instead of relying only on its training data, it looks up current, verified information first.
Why it matters for marketers: RAG solves the “outdated knowledge” problem.
A standard LLM only knows what it learned during training.
RAG lets it access your latest reports, product docs, or campaign data in real time.
Practical example: An AI chatbot for your brand that retrieves answers from your actual FAQ database instead of making up plausible-sounding responses.
8️⃣ Hallucination
📌 Definition: When an AI model generates information that is factually incorrect, fabricated, or nonsensical but presents it with the same confidence as accurate information.
Why it matters for marketers: This is the #1 risk of using AI for content.
An LLM can invent statistics, fake quotes, non-existent research papers, and incorrect product specs.
It does not know it is wrong. It simply generates the most statistically probable next word.
How to protect yourself: Never publish AI-generated claims without human verification.
Treat every factual statement as “unverified draft” until a human confirms it.
9️⃣ Parameters
📌 Definition: The internal variables that an AI model learns during training.
More parameters generally means the model can capture more complex patterns.
GPT-4 is estimated to have over 1 trillion parameters.
Why it matters for marketers: Parameter count is the “engine size” of AI.
Bigger is not always better for your use case. A 7-billion parameter model fine-tuned for your industry may outperform a trillion-parameter general model for your specific tasks.
Practical takeaway: Do not buy AI based on parameter count. Buy based on performance on YOUR task.
🔟 Training Data
📌 Definition: The dataset used to teach an AI model.
The model’s capabilities, biases, and blind spots are all direct reflections of its training data.
If the training data is biased, the model is biased.
Why it matters for marketers: Most LLMs are trained primarily on English-language internet content. This means they have Western cultural biases, limited understanding of Vietnamese market dynamics, and gaps in non-English consumer behavior data.
Watch out: When AI gives you a “best practice” for Vietnam marketing, ask: was this learned from Vietnamese data or extrapolated from US/UK patterns?
💡 AI is only as good as the data it was trained on and the data you give it. Know the source. Question the confidence.
👉 Next: Part 3 covers Application terms: API, AI Agent, Multimodal, Computer Vision, and Embedding.
❓ Frequently Asked Questions
What is AI hallucination and how do marketers avoid it?
AI hallucination is when a model generates factually incorrect information with full confidence.
Marketers avoid it by treating all AI-generated facts as unverified drafts, cross-checking statistics or quotes, & never publishing AI output without human review.
What is the difference between fine-tuning and RAG?
Fine-tuning permanently changes the model by training it on new data.
RAG temporarily gives the model access to external information during a conversation.
Fine-tuning changes what the model knows. RAG changes what the model can look up.
Why does training data matter for marketing AI?
Most AI models are trained on English-language Western internet content.
This means they carry cultural biases and may not understand Vietnamese consumer behavior, local market dynamics, or regional business practices without additional fine-tuning or RAG.
🤝 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.
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