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
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
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
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
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
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
2-1-12. 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
