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 construct the full production environment—from legal compliance to user interface—required to turn a neural network into a working product.

Join us as we:

• Secure the Foundation: We tackle Datasets and Licensing, explaining why data governance, provenance tracking, and legal compliance are the non-negotiable starting points of any system.

• Engineer the Pipeline: We break down the Training Pipeline, detailing the operational discipline required to automate preprocessing, manage distributed training, and ensure reproducibility.

• Define Success: We construct Evaluation Frameworks, moving beyond simple accuracy metrics to build systematic testing for robustness, safety, and bias mitigation.

• Orchestrate the Stack: We explore the Integrated Application Stack, visualizing how inference APIs, vector databases, caching layers, and security modules must coordinate to serve users reliably.

• Learn from Reality: We review Case Studies & Best Practices, synthesizing lessons from real-world deployments to highlight why modular design and observability are critical for long-term maintenance.

This episode serves as the comprehensive blueprint for engineers ready to integrate data, algorithms, and infrastructure into a unified, scalable system.

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Episoder(23)

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

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

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

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

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

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

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

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