Productionizing LLM Systems

What you will be able to do

• You will be able to design a task-specific evaluation suite with golden datasets, scoring metrics, and LLM-as-judge graders that reflect what users actually need.
• You will be able to build regression and online testing into your pipeline so prompt, model, and data changes ship on evidence instead of guesswork.
• You will be able to decide between prompting, retrieval-augmented generation, and fine-tuning by weighing quality, data, cost, and maintenance for a given problem.
• You will be able to add input and output guardrails that defend against prompt injection, block unsafe content, and handle model failures gracefully.
• You will be able to cut cost and latency using caching, model routing, streaming, and right-sized models without sacrificing quality you can measure.
• You will be able to instrument an LLM system with tracing, token and cost tracking, and user feedback so you can debug and improve it in production.
• You will be able to deploy model and prompt changes safely using versioning, canary releases, fallbacks, and clear service level objectives.

What is inside

4 modules, 8 lessons. Each module ends in a graded quiz and most carry an assignment.

1. 01

   Evaluation and Testing for LLM Systems

   You cannot improve or safely ship what you cannot measure, so production LLM work starts with evaluation. This module builds the full loop: golden datasets, task-matched metrics, and calibrated LLM-as-judge grading, then offline evals, CI regression gates, and online experiments. You finish able to catch quality drops before your users do.

   2 lessons · 5 quiz questions

2. 02

   Prompting, Retrieval, or Fine-Tuning

   The biggest architecture decision in an LLM system is how you get quality: better prompts, retrieval, or a trained model. This module weighs prompting and retrieval-augmented generation against supervised fine-tuning, LoRA, and preference tuning, along with the data, cost, and maintenance each one demands. You will leave with an eval-driven way to choose, and a clear sense of when fine-tuning is genuinely worth it.

   2 lessons · 5 quiz questions · assignment

3. 03

   Guardrails and Safety

   Once real inputs arrive, your model needs a safety envelope around it. This module builds that envelope in two directions. First, input and output guardrails: prompt-injection defenses tied to the OWASP Top 10 for LLM Applications, content moderation, and PII handling. Second, designing for failure: honest refusals, confidence-based abstention, fallbacks, human review for high-stakes actions, and red-teaming anchored to the NIST AI Risk Management Framework Generative AI Profile. You will leave able to reason clearly about what can go wrong in a production LLM system and about the layered controls that keep it safe.

   2 lessons · 5 quiz questions

4. 04

   Cost, Latency, Observability, and Deployment

   A system that is accurate but slow, expensive, or invisible does not survive production. This module covers the economics and speed of serving (tokens, caching, model routing, streaming, and right-sized models) and how to read tail latency honestly. You will then instrument the system with tracing and cost telemetry, and ship changes safely using versioning, canary releases, and service level objectives.

   2 lessons · 5 quiz questions · assignment