LLM Primer
LLM Primer is a structured deep dive into Large Language Models, based on a seven-book series covering foundational concepts, mathematical intuition, RAG, MCP, scalable AI systems, and AI security. It is designed for engineers and serious professionals who want real understanding rather than surface-level explanations. Each season corresponds to one book, and each episode builds technical clarity step by step. The podcast aims to help listeners understand the model and build better systems.
Episodes
Prompt Injection and Jailbreaks
This chapter examines prompt injection and jailbreak attacks, which exploit a language model's inherent inability to distinguish between authoritative developer instructions and untrusted user data. It covers the mechanics of direct and indirect injection, categorises common jailbreaking techniques, discusses the limitations of defensive prompt engineering, and outlines a layered mitigation st
Data Security and Privacy
This chapter examines data security and privacy throughout the LLM lifecycle. It explores the inherent risks of training data, such as copyright issues, personal information (PII) contamination, and data poisoning. Additionally, it details how models can leak sensitive information through memorization and extraction attacks, and outlines operational defenses for securing systems, including input r
Threat Modeling for LLM Systems
This chapter adapts traditional threat modeling frameworks (such as STRIDE, PASTA, and attack trees) specifically for the unique vulnerabilities of LLM systems. It guides defenders through identifying AI-specific assets and adversaries, and provides a step-by-step procedure for building a living threat model that can be maintained alongside the system's codeAmazon.com: LLM Primer VII AI Securi
Why AI Security Is Different
This chapter explains that AI security fundamentally and structurally differs from traditional software security. Instead of finding and patching clear bugs in readable source code, defenders must secure probabilistic models whose behaviors are driven by billions of uninterpretable weights and training data. Consequently, the security focus shifts from ensuring code correctness to managing and res
2-7-7. Hallucinations and Reliability: Managing Confident Errors
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 practical strategies like calibration and hybrid verification to align model confidence with reality.Amazon.com: LLM Primer VII AI Security: Design Safe and Robust AI Syst
2-7-6. Retrieval-Augmented Generation Risks: Securing the Knowledge Pipeline
This 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 attackers plant traps in your knowledge base, and the necessity of validating documents before they enter the model's context.Amazon.com: LLM Primer VII AI Security: Design Safe a
2-7-5. Input Validation and Output Filtering: The Defense Pipeline
This 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 ambiguity, and why output moderation is essential to catch policy violations that slip through earlier defenses.Amazon.com: LLM Primer VII AI Security: Design Safe and Robust AI Syst
2-7-4. Prompt Injection and Jailbreaks: Defending the Interpreter
This 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 systems rather than a standard bug. The episode covers jailbreaking techniques like role-playing and obfuscation, and why defense requires architectural layers rather than just better p
2-7-3. Data Security and Privacy: The AI Lifecycle
This 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 importance of treating user prompts and logs as sensitive assets.Amazon.com: LLM Primer VII AI Security: Design Safe and Robust AI System eBook : SHIMODA, SHO: Kindle Store
2-7-2. Threat Modeling for LLM Systems: A Step-by-Step Guide
This 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 expanded attack surface of API-based systems to build durable defenses.Amazon.com: LLM Primer VII AI Security: Design Safe and Robust AI System eBook : SHIMODA, SHO: Kindle Store
2-7-1. The Probabilistic Shift: Why AI Security is Different
This 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 expand the attack surface from endpoints to context, and why security must be designed into the architecture rather than patched on later.Amazon.com: LLM Primer VII AI Security: Design
2-1-12. The System Architect — Building Your Own LLM System
In 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 production environment—from legal compliance to user interface—required to turn a neural network into a working product.Join us as we:• Secure the Foundation: We tackle Datasets and L
2-1-11. The Research Frontier — Cutting-Edge Research
In 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 Transformer to overcome its fundamental limitations: computational cost, static knowledge, and isolation from the physical world.Join us as we:• Scale Efficiently: We break down Sparse Mod
2-1-10. The Trust Architecture — Safety, Ethics, & Trust
In 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 from "what the model can do" to "whether we should trust it."Join us as we:• Confront the Hallucinations: We analyze why models confidently generate false info
2-1-9. The Cost of Intelligence — Performance, Scaling, and Costs
In 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 triangle" engineers must navigate to turn a research artifact into a sustainable product.Join us as we:• Weigh the Returns: We analyze Model Size vs. Capability, discussing empi
2-1-8. The Engineering Reality — Using LLMs in Applications
In 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 knows" to "how the system behaves."Join us as we:• Architect the Conversation: We analyze Chatbots & Conversational Agents, explaining why memory management, sy
2-1-7. The Hybrid System — Beyond Next-Token Prediction
In 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, understanding images, and grounding itself in external facts.Join us as we:• Map the Meaning: We explain Embeddings, the dense vector representations that transform language into geometry,
2-1-6. From Generalist to Specialist — Fine-Tuning & Adaptation
In 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 transformed into a specialized tool capable of following instructions, coding, or offering legal advice.Join us as we:• Define the Shift: We distinguish between Pretraining (bui
2-1-5. The Industrial Pipeline — Training Large Models
In 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 through a massive, coordinated engineering process known as training.Join us as we:• Curate the Curriculum: We discuss why "more data" isn't always better, explaining t
2-1-4. The Blueprint of Intelligence — The Transformer Architecture
In 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 "self-attention" mechanism that allows models to process massive amounts of information in parallel.Join us as we:• Deconstruct the Block: We break down the essential compon
2-1-3. The Computational Engine — Neural Networks for Language
In 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 architectural evolution from simple layered networks to the breakthrough that powers modern AI: Self-Attention.Join us as we:• Build the Basics: We explain the fundamental components of neural
2-1-1. Mechanism, Not Mythology — What Is a Large Language Model?
In 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 of the past to the massive statistical systems that power modern AI.Join us as we:• Dissect the Acronym: We break down exactly what "Large" (scale), "Language" (token sequences), an
2-1-2 The Statistical Backbone — Probability, Tokens, and Text
If 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 as a probabilistic prediction task.Join us as we:• Decode the Input: We explore how raw text is converted into numerical sequences called "tokens" using subword algorithms like Byt
Recommended
Learn English B1 with Daily News | English Listening Practice
Talk About Talk - Executive & Leadership Communication Skills
English with Olivia | Slow Conversations & Vocabulary
Stand In The Circle
SLOW ENGLISH PODCAST (A1-A2) With Subtitles | Daily Videos | English With Us Podcast
Learn English A2 with Daily News | Simple English Listening Practice
پلی لیست | PlayList
Conspiracy Files with Paige Carter
Bad Friends
Learn 50 English Phrases While You Sleep | Everyday English Phrases & Vocabulary
This Past Weekend w/ Theo Von
Dateline NBC
