All Things LLM

Discover how generalist AIs become powerful specialists in this episode of "All Things LLM." Hosts Alex and AI expert Ben break down the next stage of the LLM lifecycle—customization—and unpack the practical techniques that transform foundation models into domain experts or business-ready assistants.

Learn about:

• Fine-Tuning: Why it’s essential for adapting pre-trained LLMs to specific fields, and how supervised training on labeled, high-quality data unlocks expert performance in areas like healthcare, law, or finance.
• Full Fine-Tuning vs. PEFT: The pros and cons of updating all model parameters versus using Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA, which minimize costs, storage needs, and the risk of “catastrophic forgetting.”
• Cost, Complexity, and Practical Examples: Why full fine-tuning is resource-intensive, and how companies can now adapt models more efficiently—keeping generic skills while layering on domain expertise.
• Retrieval-Augmented Generation (RAG): How RAG lets you connect LLMs to external, up-to-date, or proprietary knowledge bases—enabling truly current, factual, and trustworthy answers without retraining the model.
• When and Why to Combine Approaches: Real-world strategies for using fine-tuning and RAG together, balancing deep customization, ongoing knowledge updates, and enterprise needs.

Perfect for listeners searching for:

• Fine-tuning vs. RAG
• Parameter Efficient Fine-Tuning (PEFT)
• What is LoRA?
• RAG in enterprise AI
• How to customize a large language model
• Catastrophic forgetting in AI
• LLM use cases for business

This episode distills essential techniques and practical guidance for business leaders, AI builders, and anyone interested in the future of customizable, intelligent assistants. Subscribe and tune in now—and join us next week as Alex and Ben reveal the art of prompt engineering: how expert prompts unlock even more power from today’s language models!

All Things LLM is a production of MTN Holdings, LLC. © 2025. All rights reserved.
For more insights, resources, and show updates, visit allthingsllm.com.
For business inquiries, partnerships, or feedback, contact: [email protected]

The views and opinions expressed in this episode are those of the hosts and guests, and do not necessarily reflect the official policy or position of MTN Holdings, LLC.

Unauthorized reproduction or distribution of this podcast, in whole or in part, without written permission is strictly prohibited.
Thank you for listening and supporting the advancement of transparent, accessible AI education.

Get an insider’s look behind the curtain of modern AI with this episode of "All Things LLM." Join hosts Alex and AI expert Ben as they reveal the colossal effort, expense, and ingenuity required to take a language model from “blank slate” to foundational intelligence.

What you’ll learn:

• The massive scale of LLM training: how developers assemble and meticulously clean terabytes of text—web pages, books, code, scientific articles, forums, StackExchange, Wikipedia—to power the world's top AI models.
• Why raw internet data isn’t enough: how advanced data cleaning, filtering, deduplication, curation, and privacy-preserving techniques ensure that only high-quality, compliant, and safe data fuels the learning process.
• Self-supervised learning, explained: the difference between Causal Language Modeling (next-word prediction, used by GPT) and Masked Language Modeling (fill-in-the-blank, used by BERT)—and how these simple-sounding tasks create astonishing language abilities.
• The staggering costs: real-world examples of the multi-million dollar, energy-hungry GPU clusters and the environmental impact, with electricity and water usage statistics that highlight why this phase is “large” in every sense of the word.
• The lifecycle of AI: why this pre-training phase only produces a “base model” or “foundation model”—and what has to happen next to make it truly useful as a chatbot, assistant, or domain expert.

Perfect for listeners searching for:

• How to train a language model
• LLM data preparation
• Self-supervised learning in AI
• Environmental cost of AI
• Base models vs. fine-tuned models
• How much does training an LLM cost?
• State-of-the-art NLP podcast

Listen now to understand the monumental process behind today’s most powerful AIs—and get ready for next week’s episode, where Ben and Alex unpack the essential fine-tuning and reinforcement steps that turn generalist base models into today’s smart, helpful, responsive chatbots and assistants!

All Things LLM is a production of MTN Holdings, LLC. © 2025. All rights reserved.
For more insights, resources, and show updates, visit allthingsllm.com.
For business inquiries, partnerships, or feedback, contact: [email protected]

The views and opinions expressed in this episode are those of the hosts and guests, and do not necessarily reflect the official policy or position of MTN Holdings, LLC.

Unauthorized reproduction or distribution of this podcast, in whole or in part, without written permission is strictly prohibited.
Thank you for listening and supporting the advancement of transparent, accessible AI education.

Curious how AI language models like ChatGPT burst into the mainstream? Welcome to "All Things LLM," where hosts Alex and AI expert Ben unravel the true origins and evolution of Large Language Models. In this episode, we journey through more than a century of discoveries that paved the way for today’s groundbreaking AI.

Discover:

• The surprising roots of AI language work in 19th-century linguistics and early theories of semantics.
• Alan Turing’s pivotal questions about machine intelligence and how the famous Turing Test shaped early thinking about conversational computers.
• The birth of NLP with ELIZA in the 1960s and the transition from rule-based systems to Statistical Language Models (SLMs) in the 1980s.
• How neural networks and word embeddings revolutionized natural language processing in the 2010s, with techniques like Word2Vec and GloVe making models more “aware” of meaning and context.
• The game-changing introduction of the Transformer architecture in 2017, enabling parallel processing, self-attention, and unprecedented scaling.
• How exponential increases in data and model parameters—seen in BERT, GPT-2, GPT-3, and beyond—led to the “Age of Scale” and unlocked emergent abilities that stunned the world in 2022.

Whether you’re an AI enthusiast, developer, educator, or just want an inside look at how technologies like ChatGPT, Claude, and Gemini came to life, this episode connects the dots from foundational theories to today’s most advanced language models. Learn how decades of linguistic theory, computer science, and mathematics converged into the LLM revolution—the real story behind what feels like an overnight success.

Listen now to explore:

• NLP’s core milestones
• The science and strategy behind next-word prediction
• Why the Transformer changed everything in AI
• The timeline and key breakthroughs leading to today’s AI gold rush

Subscribe, leave a review, and join us next week for an in-depth dive into the mechanics of the Transformer—how “self-attention” drives everything modern AI can do.

All Things LLM is a production of MTN Holdings, LLC. © 2025. All rights reserved.
For more insights, resources, and show updates, visit allthingsllm.com.
For business inquiries, partnerships, or feedback, contact: [email protected]

The views and opinions expressed in this episode are those of the hosts and guests, and do not necessarily reflect the official policy or position of MTN Holdings, LLC.

Unauthorized reproduction or distribution of this podcast, in whole or in part, without written permission is strictly prohibited.
Thank you for listening and supporting the advancement of transparent, accessible AI education.

Unlock the mysteries of modern AI with "All Things LLM." In this episode, Alex and Ben break down the Transformer—the revolutionary engine powering today’s Large Language Models (LLMs) like GPT-4, Llama, and Gemini. If you’ve ever wondered how AI can both understand and generate text, this deep dive into Transformer architecture is your essential guide.

You'll discover:

• The “encoder” and “decoder” building blocks that form the foundation of every Transformer, explained in clear, accessible language.
• The three main Transformer model families—encoder-only (like BERT), decoder-only (GPT, Llama), and encoder-decoder (T5)—and how each excels at a specific type of AI task.
• Real-world examples: why BERT is great for tasks that need nuanced understanding (like text classification), while GPT dominates at creative text generation (like essays and chatbot replies), and how models like T5 and Google Translate tackle complex sequence-to-sequence challenges like translation and summarization.
• Simple analogies to help you grasp the difference between understanding, generating, and transforming text in the world of AI.
• How all these models are built with the same Transformer “blueprint,” but each is uniquely tuned for specialized capabilities.

Whether you're an AI beginner, an entrepreneur exploring automation, or a developer digging deeper into how LLMs work, this episode delivers an insider’s look at the architectural DNA of modern AI language tools.

Perfect for listeners searching for:

• How Transformers work
• Encoder vs. decoder in AI
• BERT, GPT, Llama, T5 explained
• Sequence-to-sequence modeling
• AI model types for NLP tasks

Stay tuned as we preview next week’s episode—a hands-on breakdown of “self-attention,” the magic that makes Transformers so powerful.

Subscribe and follow for more insightful explanations and up-to-the-minute news on artificial intelligence, natural language processing, and the future of LLMs—all in one place!

All Things LLM is a production of MTN Holdings, LLC. © 2025. All rights reserved.
For more insights, resources, and show updates, visit allthingsllm.com.
For business inquiries, partnerships, or feedback, contact: [email protected]

The views and opinions expressed in this episode are those of the hosts and guests, and do not necessarily reflect the official policy or position of MTN Holdings, LLC.

Unauthorized reproduction or distribution of this podcast, in whole or in part, without written permission is strictly prohibited.
Thank you for listening and supporting the advancement of transparent, accessible AI education.

Step into the rapidly evolving world of artificial intelligence with the premiere of "All Things LLM." In this debut episode, hosts Alex and AI expert Ben break down one of today's most talked-about technologies: the Large Language Model (LLM).

Are you curious about terms like ChatGPT, generative AI, or conversational AI—but not sure where to start? This episode offers a friendly, jargon-free introduction that’s perfect for AI beginners, business leaders, tech enthusiasts, or anyone looking to understand what’s behind the latest wave of language-powered AI tools.

Here’s what you’ll learn in “Hello, World! – What Exactly Is an LLM?”:

• What is a Large Language Model (LLM)?
  Understand how these advanced deep learning models are trained on massive datasets to generate human-like text, answer questions, and carry on conversations.
• How does an LLM “understand” and generate language?
  Discover why LLMs are statistical prediction engines—not sentient beings—and why their “understanding” is about recognizing patterns, not thinking like a human.
• LLM vs. Traditional AI: What’s the difference?
  Explore how LLMs differ from older AI, with the ability to handle diverse language tasks (summarization, translation, chatbots) without bespoke models for each task.
• The “black box” problem: Why don’t we fully understand LLMs?
  Ben dives into the challenges of model transparency, why even researchers find LLMs’ internal workings opaque, and what that means for trust and adoption—in fields like healthcare, finance, and beyond.
• The “ChatGPT moment” and the AI Gold Rush:
  Learn how the public launch of ChatGPT in November 2022 sparked a historic surge in AI adoption, changing how enterprises and individuals engage with artificial intelligence.

Perfect for listeners searching for:

• What is an LLM?
• Intro to large language models
• Generative AI explained
• Best AI podcasts for beginners
• Understanding ChatGPT and modern AI
• AI “black box” explained
• How do LLMs work?
• Future of artificial intelligence

Key Quotes:

• “A Large Language Model is an advanced AI system designed to understand, interpret, and generate human-like text.”
• “LLMs are not conscious—they’re powerful pattern predictors that simulate understanding based on massive training data.”
• “What makes LLMs groundbreaking is their flexibility and ability to handle complex, unstructured human language at scale.”
• “Even the people who build LLMs often don’t fully know what’s happening inside—the ‘black box’ issue.”

Stay Tuned:
Next week, Alex and Ben will trace the history of LLMs, exploring the key discoveries and milestones behind today’s breakthrough AI tech.

Subscribe, share, and join the conversation at allthingsllm.com.

Unlock the foundation of modern AI and get ready to demystify LLMs—one episode at a time!

Keywords: Large Language Model, LLM, generative AI, ChatGPT, artificial intelligence podcast, AI explained, deep learning, machine learning, conversational AI, tech for beginners, All Things LLM Podcast, Alex, Ben, Mr. Dew, allthingsllm.com

All Things LLM is a production of MTN Holdings, LLC. © 2025. All rights reserved.
For more insights, resources, and show updates, visit allthingsllm.com.
For business inquiries, partnerships, or feedback, contact: [email protected]

The views and opinions expressed in this episode are those of the hosts and guests, and do not necessarily reflect the official policy or position of MTN Holdings, LLC.

Unauthorized reproduction or distribution of this podcast, in whole or in part, without written permission is strictly prohibited.
Thank you for listening and supporting the advancement of transparent, accessible AI education.