AI Software Development
Build retrieval-augmented AI systems that are accurate, observable and ready for real users.
Most RAG demos break on real data. This course goes deep on ingestion, chunking, retrieval quality, re-ranking, evaluation, security and operations — so your LLM application works reliably for real users.
A RAG prototype can be built in an afternoon. Making it accurate, secure and maintainable on messy real-world documents is a different job — and that's what this course is about.
You'll work with realistic document sets, measure retrieval quality with a proper evaluation set, and learn the techniques that consistently improve answers: better chunking, hybrid search, re-ranking and grounded citations.
Bring your own data: live-cohort students can apply the techniques to their own (non-confidential) documents during the capstone.
Results depend on your effort, pricing and market — we teach the skills and how to sell them, not guaranteed income. Explore income streams
5 modules · 19 lessons
Multi-format ingestion, hybrid retrieval, citations and access control.
An automated test suite that scores retrieval and answers on every change.
AI Skill Educator at AIMoniPoint
Daniel has mastered building AI apps, AI automations and AI agents for small and medium-sized businesses, and has helped over 400 students globally build practical AI skills. He also manages projects as a Senior Business Data Analyst across banking, telecoms and oil & gas — and teaches you how to turn AI skills into income.
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