Opening the Black Box:
Generative AI Mechanics
Generative AI represents a fundamental shift in computing. Unlike traditional software which follows explicit rules, Generative AI operates on probability. In this module, we peel back the layers of the LLM.
The Physics of GenAI
How does ChatGPT actually "write"? It's not magic; it's math. At its core, it is predicting the next most likely chunk of text (token) based on everything it has seen before. Explore the interactive diagram below to see how Transformers, Embeddings, and Diffusion work.
The Physics of Generative AI
How models understand text, generate images, and capture meaning.
Transformer Architecture (LLMs)
The engine behind GPT, Claude, and Llama. It uses 'Self-Attention' to understand the relationship between every word in a sequence simultaneously, not just sequentially.
The basic units of text (words or sub-words). LLMs process tokens, not characters.
The amount of text the model can 'see' at once (short-term memory).
A parameter controlling randomness. High = Creative, Low = Deterministic.
Probabilistic vs. Deterministic
This is the most important concept for business leaders. Traditional software is rigid but accurate. Generative AI is flexible but can be wrong. Understanding this trade-off is key to choosing the right use cases.
Capabilities vs. Limitations
Navigating the shift from Deterministic Software to Probabilistic AI.
- Adaptability
Unlike traditional rule-based systems, LLMs handle edge cases and "fuzzy" inputs naturally. You don't need to code every possible user intent.
- Creation & Synthesis
Can generate code, summaries, or marketing copy from scratch, massively boosting productivity for knowledge workers.
The Black Box Problem
Why did the AI say that? With traditional "Glass Box" models (like Excel formulas), we can trace the logic. With Deep Learning ("Black Box"), the logic is hidden inside billions of parameters. This has huge implications for compliance and auditing.
Transparency vs. Opacity
Not all AI is created equal. Understanding the "Black Box" problem.
Black Box Models
Opaque & Complex
The decision logic is hidden within millions (or billions) of parameters. You see inputs and outputs, but the 'Why' is mathematical noise.
- High accuracy
- Handles unstructured data
- Hard to trust
- Difficult to explain failure
Batch vs. Real-Time Inference
"Inference" is the act of using a model to generate a prediction. Depending on your business need, you might run this overnight for millions of records (Batch) or instantly when a user clicks a button (Real-Time).
Inference Strategies
How AI makes predictions: Batch vs. Real-Time.
Batch Processing
Processing data in large groups (chunks) at scheduled intervals (e.g., every night). Ideal for non-urgent tasks where throughput matters more than speed.
Ready to apply this?
Now that you understand the mechanics, let's look at high-value business use cases.
Go to Module 3: Strategy & ROI