There are two exceptional videos that together strip away the "magic button" hype and break down exactly what you are looking for with systematic, technical reality:
Andrej Karpathy's "[1hr Talk] Intro to Large Language Models" (http://www.youtube.com/watch?v=zjkBMFhNj_g)
Karpathy (formerly Director of AI at Tesla and a founding member of OpenAI) provides the definitive breakdown of how generative AI works at a foundational level. He clearly explains how models are trained, why they hallucinate, and the underlying non-determinism without using corporate buzzwords.
IBM Technology's "What is Retrieval-Augmented Generation (RAG)?" (http://www.youtube.com/watch?v=T-D1OfcDW1M)
This is a concise, whiteboard-style breakdown that specifically addresses the file-finding mechanism (chunking) used to ground models in verified data.
Here is a breakdown of the core mechanics you mentioned, moving from basic generation to the crucial concept of non-determinism:
1. How Generative AI Works (Next-Token Prediction)
At its core, a Large Language Model (LLM) does not "think" or retrieve stored facts like a traditional database. It is simply a massive statistical engine predicting the next word (or "token") in a sequence. It calculates the probability of what word should logically follow the previous words based on the vast, compressed patterns of text it absorbed during its initial training.
2. Hallucinations
Because an LLM is a probabilistic engine, it can confidently generate plausible-sounding but entirely false statements. It hallucinates when the statistical probability of a word sequence is high, but the factual accuracy is zero. This is precisely why relying on unverified "automated trust" or "vibe coding" platforms introduces severe security and operational risks. Without an external verification framework, the model is just guessing eloquently.
3. Finding Information in Files (Chunking)
To fix the hallucination problem, developers use Retrieval-Augmented Generation (RAG). Instead of asking the AI to answer from its compressed memory, the system takes your internal documents and breaks them into smaller "chunks" (paragraphs or sections). These chunks are converted into numerical coordinates called vectors. When you ask a question, the system mathematically searches for the most relevant chunks, retrieves them, and feeds them to the AI along with your prompt. This creates a verifiable audit trail, ensuring the AI bases its answer strictly on the retrieved text rather than fabricating information.
4. Why It's Not Deterministic (Temperature & Probability)
When building rigorous systems where systematic quality is expected, understanding non-determinism is critical. Standard Python scripts for office automation are deterministic: the exact same input guarantees the exact same output. Generative AI is inherently probabilistic.
When the model predicts the next word, it generates a list of possible words and assigns a probability to each. A parameter called Temperature controls how the model makes its final selection from that list:
Low Temperature (e.g., 0.0): The model always picks the word with the highest probability. This makes the output highly predictable and nearly deterministic (though entirely deterministic behavior is still difficult to guarantee due to floating-point math on GPUs).
High Temperature (e.g., 0.8): The probabilities are flattened out, giving lower-ranked words a higher chance of being selected. This introduces randomness, meaning the exact same prompt will yield a completely different response every time.
To truly understand how Temperature dictates this non-deterministic behavior, you can explore the interactive simulation below.
Visualizing Non-Determinism
This interactive widget simulates the "next-token prediction" phase of an LLM. By adjusting the Temperature slider, you can observe how the probability distribution shifts. A temperature of 0 sharpens the focus purely on the top choice, while higher temperatures distribute the probability across multiple options, introducing the inherent randomness of generative models.
