LLM Primer

LLM-PRIMER
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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.

  1. 1D AGO

    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 System eBook : SHIMODA, SHO: Kindle Store

    16 min

  2. 1D AGO

    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 and Robust AI System eBook : SHIMODA, SHO: Kindle Store

    35 min

  3. 1D AGO

    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 System eBook : SHIMODA, SHO: Kindle Store

    29 min

  4. 1D AGO

    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 prompts. Amazon.com: LLM Primer VII AI Security: Design Safe and Robust AI System eBook : SHIMODA, SHO: Kindle Store

    37 min

  5. 1D AGO

    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

    25 min

  6. 1D AGO

    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

    30 min

  7. 1D AGO

    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 Safe and Robust AI System eBook : SHIMODA, SHO: Kindle Store

    36 min

  8. 2D AGO

    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 Licensing, explaining why data governance, provenance tracking, and legal compliance are the non-negotiable starting points of any system. • Engineer the Pipeline: We break down the Training Pipeline, detailing the operational discipline required to automate preprocessing, manage distributed training, and ensure reproducibility. • Define Success: We construct Evaluation Frameworks, moving beyond simple accuracy metrics to build systematic testing for robustness, safety, and bias mitigation. • Orchestrate the Stack: We explore the Integrated Application Stack, visualizing how inference APIs, vector databases, caching layers, and security modules must coordinate to serve users reliably. • Learn from Reality: We review Case Studies & Best Practices, synthesizing lessons from real-world deployments to highlight why modular design and observability are critical for long-term maintenance. This episode serves as the comprehensive blueprint for engineers ready to integrate data, algorithms, and infrastructure into a unified, scalable system.

    39 min
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About

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.

Information

  • Creator
    LLM-PRIMER
  • Years Active
    2K
  • Episodes
    19
  • Rating
    Clean
  • Copyright
    © LLM-PRIMER
  • Show Website
    LLM Primer