Module 26: Production, Safety & Ethics

Chapter Overview

Moving an LLM prototype from a Jupyter notebook to a production system introduces an entirely new category of engineering and governance challenges. Latency, scalability, security, reliability, fairness, and regulatory compliance all demand attention long before launch. A model that performs perfectly on a benchmark can still fail catastrophically in production if its deployment architecture cannot handle traffic spikes, if adversarial users can extract its system prompt, or if its outputs perpetuate harmful biases at scale.

This module covers the full production lifecycle for LLM applications. It begins with deployment architecture (FastAPI, LitServe, Docker, cloud services) and frontend frameworks (Gradio, Streamlit, Chainlit). It then addresses scaling, performance optimization, and production guardrails (NeMo Guardrails, Llama Guard, ShieldGemma). The operational layer follows with LLMOps practices including prompt versioning, A/B testing, feedback loops, and data flywheels.

The second half of the module shifts to safety and ethics. It covers the OWASP Top 10 for LLMs, prompt injection defenses, hallucination detection and mitigation, bias measurement, model cards, and environmental impact. The regulatory landscape (EU AI Act, GDPR, US executive orders) and enterprise governance frameworks (NIST AI RMF, ISO 42001) are examined alongside practical audit strategies. The module concludes with licensing, intellectual property, privacy, and the emerging field of machine unlearning.

Learning Objectives

Design and deploy LLM applications using FastAPI, LitServe, Docker Compose, and major cloud platforms (AWS, GCP, Azure)
Build interactive frontends with Gradio, Streamlit, Chainlit, and the Vercel AI SDK
Implement production guardrails using NeMo Guardrails, Llama Guard, and content safety classifiers
Establish LLMOps workflows with prompt versioning, A/B testing, online evaluation, and data flywheels
Defend against OWASP Top 10 LLM threats including prompt injection, jailbreaking, and data exfiltration
Detect and mitigate hallucinations using self-consistency, citation verification, and constrained generation
Measure and reduce bias in LLM outputs through systematic auditing and model cards
Navigate the EU AI Act, GDPR, and US regulatory frameworks for AI governance
Implement enterprise risk governance using NIST AI RMF, ISO 42001, and SR 11-7 frameworks
Understand model licensing taxonomies, IP ownership, and differential privacy for LLM training data
Apply machine unlearning techniques for GDPR compliance, copyright removal, and safety alignment

Prerequisites

Module 09: LLM APIs (chat completions, message formatting, model parameters)
Module 10: Prompt Engineering (prompt design, structured outputs, chain-of-thought)
Module 19: Retrieval-Augmented Generation (RAG pipelines, vector stores)
Module 25: Evaluation and Observability (metrics, tracing, monitoring)
Familiarity with Python web frameworks, Docker, and cloud deployment basics

Chapter Overview

Learning Objectives

Prerequisites

Sections