Description

Large Language Models: Building Blocks & Challenges Hosted by Nathan Rigoni

In this episode we dive into the heart of today’s AI—large language models (LLMs). What makes these gigantic text‑predictors tick, and why do they sometimes hallucinate or run into bias? We’ll explore how LLMs are trained, what “next‑token prediction” really means, and the tricks (chain‑of‑thought prompting, reinforcement learning) that turn a raw predictor into a problem‑solving assistant. Can a model that has never seen a question truly reason to an answer, or is it just clever memorization?

What you will learn

• The core components of an LLM: tokenizer, encoder, transformer blocks, and the softmax decoder.
• Why training at terabyte‑scale data and quintillion‑level token iterations is required for emergent abilities.
• How chain‑of‑thought prompting and the REACT framework give models a “scratch‑pad” for better reasoning.
• The role of fine‑tuning and reinforcement learning from human feedback in shaping model behavior.
• Key pitfalls: lack of byte‑level tokenization, spatial reasoning limits, Western‑biased training data, and context‑window constraints (from ~128 k tokens to ~2 M tokens).

Resources mentioned

• Tokenization basics (see the dedicated “NLP – Tokenization” episode).
• Auto‑encoder fundamentals (see the “NLP – Auto Encoders” episode).
• Papers on chain‑of‑thought prompting and REACT agents (discussed in the episode).
• Information on context‑window sizes and scaling trends (e.g., 128 k → 2 M tokens).

Why this episode matters
Understanding LLM architecture demystifies why these models can generate coherent prose, write code, or answer complex queries—yet also why they can hallucinate, misinterpret spatial concepts, or inherit cultural bias. Grasping these strengths and limits is essential for anyone building AI products, evaluating model outputs, or simply wanting to use LLMs responsibly.

Subscribe for more AI deep dives, visit www.phronesis‑analytics.com, or email nathan.rigoni@phronesis‑analytics.com.

Keywords: large language models, next‑token prediction, tokenizer, transformer, chain of thought, REACT framework, reinforcement learning, context window, AI hallucination, model bias.