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AI Software Development

RAG & LLM Applications in Production

Build retrieval-augmented AI systems that are accurate, observable and ready for real users.

  • Advanced
  • 6 weeks · ~40 hours
  • 5 modules · 19 lessons
  • Self-paced · Live cohort
  • New course — reviews coming soon

About this course

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.

From demo to production

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.

Who it's for

  • Developers who have built a basic LLM app and want production quality
  • Data engineers responsible for AI document pipelines
  • Tech leads evaluating RAG architectures

What you'll learn

  • Design ingestion pipelines for PDFs, web pages and structured data
  • Choose chunking and embedding strategies for your data
  • Implement hybrid search, metadata filtering and re-ranking
  • Build an evaluation set and measure retrieval and answer quality
  • Defend against prompt injection and data leakage
  • Monitor quality, latency and cost in production

Ways to earn with these skills

  • Charge premium rates for reliable AI assistants
  • Offer knowledge-base set-up as a packaged service
  • Win contracts for AI document assistants

Results depend on your effort, pricing and market — we teach the skills and how to sell them, not guaranteed income. Explore income streams

Prerequisites

  • Solid Python skills
  • Experience calling an LLM API
  • Basic SQL and familiarity with Docker

Curriculum

5 modules · 19 lessons

  1. Why naive RAG fails30 min
  2. Reference architecture for production RAG40 min
  3. Choosing a vector database35 min

Projects you'll build

Enterprise document assistant

Multi-format ingestion, hybrid retrieval, citations and access control.

RAG evaluation harness

An automated test suite that scores retrieval and answers on every change.

Tools covered

  • Python
  • LlamaIndex
  • LangChain
  • pgvector
  • Qdrant
  • Cohere Rerank
  • Langfuse
  • Docker

Your instructor

Daniel Makonor, AI Skill Educator at AIMoniPoint, smiling in a grey hoodie beside a podcast microphone

Daniel Makonor

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.

Read Daniel's story

Pricing

Course price

$549

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Ask a question first
  • 5 modules · 19 lessons
  • 6 weeks · ~40 hours
  • 2 hands-on projects
  • Formats: Self-paced, Live cohort
  • Certificate of completion

Secure checkout. See the refund policy.

Prices are shown in CAD, USD, GBP and EUR — learners elsewhere (e.g. in Africa or Asia) can use USD. Community and group pricing is available — get in touch.

Frequently asked questions

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