2-1-5. The Industrial Pipeline — Training Large Models
LLM Primer17 Helmi

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 the critical steps of deduplication, filtering, and balancing diverse sources like web text, books, and code.

• Minimize the Surprise: We break down the mathematical objective of Cross-Entropy Loss and the optimization algorithm Gradient Descent, revealing how billions of parameters are nudged iteratively to improve prediction accuracy.

• Distribute the Load: We examine the physical infrastructure required for training, detailing how strategies like Data Parallelism and Model Parallelism allow engineers to split massive models across thousands of GPUs.

• Balance the Learning: We analyze the risks of Overfitting (memorizing data) versus Underfitting (failing to learn patterns), and how regularization ensures a model can generalize to new, unseen text.

This episode reveals that training an LLM is not just a math problem, but a large-scale systems engineering challenge.

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Jaksot(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 Helmi 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 Helmi 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 Helmi 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 Helmi 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 Helmi 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 Helmi 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 Helmi 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 Helmi 38min