Overview
IBM Technology builds on foundational AI concepts to explain Large Language Models specifically — what they are, how they're built, and why businesses care about them. This bridges the gap between general AI understanding and the generative AI tools most people encounter today.
Key Takeaways
What LLMs Are
A type of Generative Pre-trained Transformer (GPT) that produces human-like text and code. They're foundation models trained on massive unlabeled datasets to produce generalizable, adaptable output.
Scale Matters
GPT-3 was trained on 45 terabytes of data with 175 billion parameters. This scale is what enables the "emergent" capabilities that make modern AI tools so powerful.
Three Core Components
- Data — Massive datasets, potentially petabytes of text
- Architecture — Transformer neural networks designed to understand context
- Training — Iterative parameter adjustment to improve predictions
How Transformers Work
Transformers understand context by considering how each word relates to every other word in a sentence. During training, the model learns to predict the next word, adjusting its internal parameters to improve accuracy.
Fine-Tuning
Models can be fine-tuned on smaller, specific datasets to become experts at particular tasks — this is how general-purpose models get specialized for specific use cases.
Business Applications
- Customer Service — Intelligent chatbots handling customer queries
- Content Creation — Articles, emails, social media posts, video scripts
- Software Development — Code generation and review
Practitioner Notes
If you're in healthcare security, here's what stands out:
Data scale has security implications
When IBM mentions "45 terabytes of training data," think about what's in that data. LLMs trained on internet-scale data inevitably contain sensitive information, biased content, and potentially copyrighted material. For healthcare, this raises questions: Was PHI in the training data? Could the model regurgitate something it shouldn't?
Fine-tuning is where your risk surface expands
The video mentions fine-tuning on "smaller, specific datasets." In healthcare, this is where organizations get into trouble — fine-tuning on internal data without proper governance. Every fine-tuning dataset is a potential data exposure if not handled correctly.
"Next word prediction" explains hallucinations
Understanding that LLMs are fundamentally predicting the next likely token helps explain why they hallucinate. They're not retrieving facts — they're generating plausible text. This is critical context when evaluating AI tools for clinical or operational use.
The business applications section is where shadow AI lives
Customer service, content creation, code generation — these are exactly where employees start using unauthorized AI tools. Your acceptable use policies need to address each of these categories specifically.
Continue Learning
This is the second resource in the AI Foundations learning path.