Interpretability and Production
Explore how to design AI systems that work reliably and responsibly. Understand safety risks like prompt injection, bias measurement, adversarial attacks, and the importance of mechanistic interpretability. Learn production best practices including latency, cost, reliability, observability, and guardrails to maintain trust and compliance.
We'll cover the following...
- What are the main safety risks of LLMs and how are they categorized?
- What is bias in LLMs and how do you measure and mitigate it?
- What are adversarial attacks and jailbreaks?
- What is mechanistic interpretability and why does it matter?
- What is a model card and what should it contain?
- What are the production best practices for deploying LLMs?
- How would you implement a simple output guardrail?
- What’s next?
Every lesson in this course has built toward a single practical goal: helping you design, build, and ship AI systems that work. This lesson addresses the harder question: what does it mean for a system to work responsibly and reliably in the real world? Safety, interpretability, and production engineering are not afterthoughts. They are the factors that determine whether a system stays in production or gets pulled, whether a company builds trust with users or destroys it, and increasingly, whether a system is legally compliant.
Safety and capability are not as opposed as they first appear. The most capable deployed systems are safe, not despite their safety measures, but because safety constraints shape training and deployment in ways that improve consistency and reliability. A model that refuses to hallucinate answers it does not know is more useful in production than one that confidently fabricates.
What are the main safety risks of LLMs and how are they categorized?
The OWASP LLM Top 10 (2025 edition) is the industry reference for LLM application risks. The top risks you need to know:
Prompt injection: Attacker-controlled content in the model’s input causes it to execute unintended instructions. Covered in depth in Lesson 10.
Insecure output handling: The model’s output is used directly without validation, for example, generated SQL executed against a database, or generated HTML injected into a page without sanitization.
Training data poisoning: Malicious data inserted into the pretraining or fine-tuning corpus causes the model to produce subtly wrong or harmful outputs, often in targeted scenarios. ...