LLM Primer

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 is a technology podcast hosted by LLM-PRIMER, with 23 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

LLM-PRIMER hosts LLM Primer, a technology show with 23 episodes published.

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  LLM Primer

  Prompt Injection and Jailbreaks

  Jul 7, 202645 min

  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 strategy to better defend production systems. Amazon.com: LLM Primer VII AI Security: Defending LLM Systems Against Prompt Injection, Jailbreaks, and Adversarial Threats: 9798185644065: SHIMODA, SHO: Books

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  LLM Primer

  Data Security and Privacy

  Jul 6, 202633 min

  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 redaction pipelines, encryption, tenant isolation, and data retention policies. Amazon.com: LLM Primer VII AI Security: Defending LLM Systems Against Prompt Injection, Jailbreaks, and Adversarial Threats: 9798185644065: SHIMODA, SHO: Books

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  LLM Primer

  Threat Modeling for LLM Systems

  Jul 6, 202655 min

  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 code Amazon.com: LLM Primer VII AI Security: Defending LLM Systems Against Prompt Injection, Jailbreaks, and Adversarial Threats: 9798185644065: SHIMODA, SHO: Books

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  LLM Primer

  Why AI Security Is Different

  Jul 6, 202649 min

  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 restricting an unbounded range of unpredictable inputs and outputs. Amazon.com: LLM Primer VII AI Security: Defending LLM Systems Against Prompt Injection, Jailbreaks, and Adversarial Threats: 9798185644065: SHIMODA, SHO: Books

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  LLM Primer

  2-7-7. Hallucinations and Reliability: Managing Confident Errors

  Feb 18, 202616 minS0

  <p>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.</p>

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