From Linguistics to Large Language Models with Chris Brousseau

Feb 10, 2026

Ask episode

Chapters

Transcript

Episode notes

Chris Brousseau, co-author of LLMs in Production and VP of AI at Veox AI, joins us to peek under the hood of large language models and explore why getting them into production remains one of the hardest challenges in the field.

We start with Chris’s journey from linguistics and translation into machine learning, tracing how a graduate seminar on Python and machine translation in 2017 led him into the world of NLP. His background in semiotics and how meaning is created in language provides a grounding for understanding what LLMs can and can’t do - and why they produce useful results despite having no understanding of semantics.

From there, we dive into the fundamentals - transformers, tokenization, word embeddings, context windows, and the training pipeline from self-supervised learning through reinforcement learning (PPO and GRPO) to supervised fine-tuning. Chris shares lessons from deploying billion-parameter models at MasterCard and JP Morgan Chase, including the gap between demos and production systems and the operational challenges that come with deploying models at scale.

We also explore hallucinations, the evolution from prompt engineering to context engineering, how logic manipulation and context-free grammars can make a 2023-era 7B model outperform frontier models at math, and where agents and code-based tool calling are heading in 2026.

Topics include:

Chris’s journey from linguistics and translation into machine learning
The gap between demos and production: why deploying LLMs is uniquely hard
What LLMs actually are: autoregressive transformers, tokenization, embeddings, and context windows
Stochastic parrots and semiotics: why LLMs have syntax but no semantics
Emergent behavior and the key insights of the Attention is All You Need paper
The training pipeline: self-supervised learning, RLHF (PPO vs GRPO), and supervised fine-tuning
Hallucinations and the fundamental challenge of evaluating language model outputs
From prompt engineering to context engineering
Logic manipulation and context-free grammars: making small models outperform frontier ones
LoRA, distillation, and quantization: running and adapting your own models
Agents, Code Mode versus MCP, and practical techniques for running your own models

Throughout the conversation, Chris demonstrates how a linguistics background provides a unique lens for understanding both the strengths and fundamental limitations of large language models - and why bridging the gap between language research and computer science could unlock the next wave of progress.

Show Links