Clear lessons, dense practice
Every lesson opens with an industry situation, then uses diagrams, worked examples, mistakes, and grounded analogies.
Reading signals
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Independent courseSpecialization
LLM, RAG & AI Applications
Turn an impressive demo into an evaluated, secure, and observable AI product.
What you will be able to do
- Design prompts, structured outputs, retrieval, reranking, and grounded response flows.
- Measure retrieval, answer quality, safety, latency, and cost separately.
- Defend against prompt injection, data exposure, unsafe actions, and unreliable model output.
Before you start
Prerequisites guide your order; they do not block purchase. The opening recap tells you exactly what to refresh.
- Python for Data Science & ML
- NLP fundamentals recommended
Adaptive casework
Choose one industry—or combine several
Concepts and assessment stay stable. Stories, datasets, constraints, and project language adapt to your domains.
Honest progression
Tasks normally gate the next lesson. You may self-attest and continue; verified completion still requires passed evidence.
Interview transfer
Practice covers transformers, RAG, evaluation, prompt injection, LLM operations, then moves into timed prompts and project defense.
Portfolio evidence
Build and red-team a cited RAG assistant with an evaluation suite and fallback policy.
Keep the brief, code, tests, decision log, failure analysis, and defense script—the artifacts an interviewer can inspect.
Ready when you are
Ready to start LLM, RAG & AI Applications?
Create your account, choose your industry context, and begin with the short prerequisite check.