Hosted by LLM-PRIMER · 🇺🇸 US · EN · 19 episodes
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LLM Primer is a structured deep dive into Large Language Models, based on a seven-book series covering everything from foundational concepts and mathematical intuition to RAG, MCP, scalable AI systems, and AI security.This podcast is built for engineers and serious professionals who want real understanding—not surface-level explanations.Each season corresponds to one book. Each episode builds technical clarity step by step.Understand the model. Build better systems.
LLM-PRIMER hosts LLM Primer, a technology show with 19 episodes published.
This episode covers Chapter 7, examining why Large Language Models confidently generate false information. We discuss the probabilistic nature of "hallucinations," the dangerous gap between fluency and correctness, and p
Show notesThis episode covers Chapter 6, focusing on the security implications of connecting models to external data (RAG). We discuss how this introduces new trust boundaries, the dangers of malicious document injection where att
Show notesThis episode covers Chapter 5, detailing how to build disciplined pipelines around an AI model. We discuss strategies for sanitizing user inputs to catch attacks early, the importance of structured prompting to reduce am
Show notesThis episode explores Chapter 4, detailing how attackers manipulate model behavior through crafted inputs like instruction overrides. We discuss why prompt injection is an inherent property of instruction-following syste
Show notesThis episode breaks down Chapter 3, tracking data risks from training to deployment. We discuss how models can memorize sensitive training data, the subtle dangers of leakage through generated outputs, and the critical i
Show notesThis episode covers the systematic approach of Chapter 2, moving beyond vague security worries to concrete risk analysis. We discuss how to identify unique AI assets—like prompts, logs, and retrieval indexes—and map the
Show notesThis episode dives into Chapter 1, exploring why traditional security measures fail when applied to Large Language Models. We discuss the fundamental shift from deterministic code to probabilistic behavior, how LLMs expa
Show notesIn this episode, we bring every previous concept together to answer the ultimate practical question: How do you actually build a complete LLM system from scratch? We move beyond the model itself to construct the full pro
Show notesIn this episode, we look beyond the current generation of models to explore the experimental architectures and learning paradigms that will define the future of AI. We analyze how researchers are redesigning the Transfor
Show notesIn this episode, we address the critical challenge of turning a powerful probabilistic system into a reliable product. We explore why engineering capability must be matched with ethical responsibility, shifting the focus
Show notesIn this episode, we face the economic and physical realities of deploying AI. A model’s theoretical capability matters little if it is too slow, too expensive, or too power-hungry to run. We explore the "tradeoff triangl
Show notesIn this episode, we step out of the theoretical lab and into the messy reality of production. We explore how a raw Large Language Model is transformed into a reliable product, shifting the focus from "what the model know
Show notesIn this episode, we challenge the idea that Large Language Models are just text generators. We explore how modern AI extends beyond simple prediction to become a reasoning engine capable of searching databases, understan
Show notesIn this episode, we tackle the critical difference between a model that knows "about" everything and one that can actually do a specific job. We explore the adaptation phase, where a raw, pretrained generalist is transfo
Show notesIn this episode, we move from the theoretical blueprint of the Transformer to the operational reality of building a Large Language Model. We explore how an empty mathematical shell is transformed into a capable system th
Show notesIn this episode, we explore the specific architectural breakthrough that made the current AI revolution possible. We move from general neural network theory to the concrete blueprint of the Transformer, examining the "se
Show notesIn this episode, we open the hood of the machine. Having established that language modeling is a probability game, we now examine the actual computational structures that make learning possible. We trace the architectura
Show notesIn this premiere episode, we strip away the marketing hype to answer a fundamental question: What exactly is a Large Language Model? We move beyond the buzzwords to explore the shift from the rigid, rule-based software o
Show notesIf the first episode defined what an LLM is, this episode explains how it actually processes information. We dive into the mathematical framework that transforms human language into structured data, reframing creativity
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LLM Primer is hosted by LLM-PRIMER. The show is categorised under technology and has published 19 episodes.
LLM Primer has published 19 episodes.
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