2-1-7. The Hybrid System — Beyond Next-Token Prediction
LLM Primer17 Feb

2-1-7. The Hybrid System — Beyond Next-Token Prediction

In this episode, we challenge the idea that Large Language Models are just text generators. We explore how modern AI extends beyond simple prediction to become a reasoning engine capable of searching databases, understanding images, and grounding itself in external facts.

Join us as we:

• Map the Meaning: We explain Embeddings, the dense vector representations that transform language into geometry, allowing computers to understand that "king" is to "man" what "queen" is to "woman".

• Bridge the Gap: We contrast Generation (synthesizing new ideas) with Retrieval (accessing stored facts), showing how hybrid models combine the best of both worlds.

• Fix the Memory: We break down Retrieval-Augmented Generation (RAG), a critical architecture that connects frozen models to up-to-date external databases to improve accuracy and reduce hallucinations.

• Expand the Senses: We look at Multimodal Extensions, revealing how models are learning to "see" and "hear" by aligning visual and audio data within the same mathematical space as text.

This episode reveals how we are moving from closed, static models to open, dynamic ecosystems.

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Episoder(23)

Prompt Injection and Jailbreaks

Prompt Injection and Jailbreaks

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. I...

7 Jul 45min

Data Security and Privacy

Data Security and Privacy

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 d...

7 Jul 32min

Threat Modeling for LLM Systems

Threat Modeling for LLM Systems

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...

6 Jul 54min

Why AI Security Is Different

Why AI Security Is Different

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 sec...

6 Jul 49min

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

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