2-1-3. The Computational Engine — Neural Networks for Language
LLM Primer17 Feb

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 networks—linear layers, nonlinear activation functions (like ReLU and GELU), and embeddings—that transform discrete tokens into rich vector representations.

• Trace the History: We follow the progression from rigid Feedforward Networks to Recurrent Neural Networks (RNNs), analyzing why earlier systems struggled with memory and long-range dependencies.

• Reveal the Game Changer: We introduce Self-Attention, the mechanism that replaced sequential processing with parallel interaction, allowing models to "see" the entire context at once.

• Optimize the Learning: We touch on how billions of parameters are actually adjusted using Gradient Descent and backpropagation to minimize error and "learn" language patterns.

This episode bridges the gap between statistical theory and the specific architecture—the Transformer—that we will dismantle in the next episode.

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Episoder(19)

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

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 an...

19 Feb 16min

2-7-6. Retrieval-Augmented Generation Risks: Securing the Knowledge Pipeline

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...

19 Feb 34min

2-7-5. Input Validation and Output Filtering: The Defense Pipeline

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 pr...

18 Feb 29min

2-7-4. Prompt Injection and Jailbreaks: Defending the Interpreter

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 instruc...

18 Feb 37min

2-7-3. Data Security and Privacy: The AI Lifecycle

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...

18 Feb 25min

2-7-2. Threat Modeling for LLM Systems: A Step-by-Step Guide

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 ...

18 Feb 29min

2-7-1. The Probabilistic Shift: Why AI Security is Different

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 beh...

18 Feb 36min

2-1-12. The System Architect — Building Your Own LLM System

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 co...

17 Feb 38min

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