2-1-6. From Generalist to Specialist — Fine-Tuning & Adaptation
LLM Primer17 Helmi

2-1-6. From Generalist to Specialist — Fine-Tuning & Adaptation

In this episode, we tackle the critical difference between a model that knows "about" everything and one that can actually do a specific job. We explore the adaptation phase, where a raw, pretrained generalist is transformed into a specialized tool capable of following instructions, coding, or offering legal advice.

Join us as we:

• Define the Shift: We distinguish between Pretraining (building broad linguistic competence) and Fine-Tuning (refining behavior for specific tasks), explaining how reusing existing knowledge saves massive amounts of compute.

• Compare Strategies: We contrast Parameter-Level Adaptation (permanently updating model weights) with Prompt-Based Adaptation (steering the model through context without changing its internal structure).

• Align the Behavior: We discuss Instruction Tuning, the crucial process of training models on instruction-response pairs so they learn to obey commands rather than just autocomplete sentences.

• Specialize the Knowledge: We examine Domain-Specific Tuning, showing how models are recalibrated for high-stakes fields like medicine or finance by immersing them in specialized technical corpora.

This episode explains how we bridge the gap between a model that can write fluent English and a system that actually solves your specific problem.

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Jaksot(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...

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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...

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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...

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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...

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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...

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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...

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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