⭐ Recommended Building LLM-Powered Apps: RAG & Agents
Build production-grade AI applications using large language models. Cover vector databases, retrieval-augmented generation (RAG), autonomous agents, tool use, evaluation, and deployment patterns.
Oleksii Anzhiiak
Software Architect, Senior .NET Engineer & Co-Founder
By the end you'll be able to
- Build a working RAG system — embeddings, retrieval, grounded generation
- Build a multi-step agent that uses tools, memory, and feedback loops
- Evaluate AI systems honestly — when do they actually work vs when do they just look like they do
- Reason about cost, latency, and failure modes for production AI
- Ship the kind of AI app that justifies the 2026 hiring premium
Is this course for you?
This is right for you if you…
- You're a Senior or Senior-trajectory engineer who has shipped real products and now wants the AI dimension
- You've worked through #21 (Prompt Engineering) and want to take it from 'workflow' to 'product'
- You can read API docs, debug with telemetry, and reason about failure modes — those skills DON'T disappear when AI is in the loop
Don't take this course if you…
- You're starting from scratch in coding — RAG and agents are NOT a beginner topic. Foundation first (course #1, #11, or #15)
- You haven't done #21 — Prompt Engineering is a prerequisite. Building agents on shaky prompt skill is fragile
- You expect off-the-shelf success — RAG and agents are messy in real conditions, and the course teaches you to reason about that mess, not avoid it
Who teaches this
Oleksii Anzhiiak
Software Architect, Senior .NET Engineer & Co-Founder
Oleksii Anzhiiak is a Software Architect, Senior .NET Engineer, and Co-Founder of ToyCRM.com and ProfectusLab. With over 15 years of experience, he specializes in distributed systems, cloud infrastructure, high-load backend development, and identity platforms. Oleksii designs complex architectures, builds secure authentication systems, and develops modern engineering education programs that help students achieve real career results.
Currently leads architecture for ToyCRM.com — a multi-tenant CRM platform built on .NET by our team. The same patterns and design decisions used there appear directly in the courses: identity & auth, distributed services, code review culture. You learn from engineers actively shipping production code, not from a textbook.
Syllabus
Eight modules to build and ship production LLM applications:
- 1 Module 1: LLM internals — tokenization, context windows, temperature, sampling strategies
- 2 Module 2: Vector databases — embeddings, similarity search, Qdrant, Pinecone, pgvector
- 3 Module 3: RAG pipelines — document loading, chunking strategies, retrieval evaluation
- 4 Module 4: Advanced RAG — hybrid search, re-ranking, query rewriting, HyDE
- 5 Module 5: AI agents — ReAct pattern, tool use, memory, multi-step planning
- 6 Module 6: Multi-agent systems — agent orchestration, handoffs, shared state
- 7 Module 7: Evaluation and observability — LLM evals, tracing, LangSmith, cost monitoring
- 8 Module 8: Production deployment — streaming APIs, caching, load balancing, failover
Prerequisites
Python Fundamentals or Introduction to C# / C# Pro. AI-Powered .NET Development is highly recommended.
Python or C#/.NET experience required. Familiarity with REST APIs and basic AI concepts.
What you'll build
You leave with TWO working AI products you built end-to-end — a RAG system that answers questions over your own document set without hallucinating, and an agent that handles a multi-step task using tools. The portfolio piece that gets you on the shortlist for 2026's highest-paying engineering roles.
- RAG architecture: embeddings, vector DBs, retrieval, generation
- Agents: planning, tool use, memory, evaluation
- Frameworks: LangChain, LlamaIndex, OR raw SDK — and when to choose which
- Evaluation: golden sets, hallucination tests, regression suites
- Cost / latency / failure-mode reasoning for production AI
Where this fits in your career
Read alongside this course
OpenSpec in 2026: The Operating System for Spec-Driven Development
Six weeks ago I installed @fission-ai/openspec. Yesterday I shipped a 14-file change in 90 minutes from a 200-line spec, in a brownfield codebase three engineers have been editing for two years — no merge conflicts, no review escalation. This is the senior-architect deep-dive on why OpenSpec is the first SDD tool that doesn't collapse under production reality.
Evals in 2026: The Test Suite for Systems That Aren't Deterministic
Your AI feature worked yesterday and fails today. No code change, no prompt change, no model change. That's what life without evals looks like. This is the third leg of the spec → context → evals trinity — and the discipline most teams skip.
Spec-Driven Development: When Your Spec Becomes the Codebase
I haven't written a function by hand in two months — and the codebase has never been healthier. Here's how spec-driven development changed what 'engineering work' means in 2026, the rules that keep the discipline honest, and where it still falls apart.
First lesson on us. Decide after meeting your instructor.
Sit in on the first session. If after lesson 1 you decide the instructor isn't the right fit, you don't pay for it — and no awkward conversation. (Trial offer applies to courses with more than 5 lessons; this one qualifies.)
Pricing & what's included
What's included
- 16 live sessions × 2 hours each — taught by the instructor, not a recording playback
- Slide deck for every session — yours to keep and refer back to
- Working code files and any data files used in class — cloned to your machine
- Weekly homework with personal code review from the instructor
- Recording on request — give the teacher a heads-up ("can't make Tuesday, please record it") and the session is recorded for you
Frequently asked questions
How much time per week will this take?
Plan for the live sessions plus roughly 1.5–2 hours of practice per session. Most students who finish on schedule put in 4–6 hours a week total. If you put in less, you still finish — it just takes longer.
What if I miss a class?
Tell the teacher BEFORE the session — "I can't make Tuesday, please record it" — and the teacher records that lesson and sends it to you. Recordings aren't a default catch-up archive; they're produced on request when you give a heads-up. After the lesson you do the homework, bring questions to the next session.
What's included in the price?
Live sessions with the teacher; the slide deck for each session (yours to keep); the working code files and any data files used in class; weekly homework with code review; and recordings of the sessions you give advance notice for. Anything beyond that — a certificate, alumni access, mock interviews — is listed explicitly in "What's included" above the FAQ for the courses where it applies.
What if I sign up and the teacher isn't a fit?
For courses with more than 5 lessons, the first session is effectively a trial — if after lesson 1 you decide the teacher isn't a fit, you don't pay for it. We'd rather you walk away after one lesson than push through 9 weeks of bad chemistry. (Courses with 5 or fewer lessons are short enough that the standard pricing applies — the trial offer doesn't make sense at that length.)
Can I get a refund partway through?
Yes, while more than half the course is still unused — i.e. you've attended fewer than 50% of the lessons. The refund covers the unused portion proportionally. Past the halfway point we don't refund, on the assumption that the value has been delivered.
Can I switch the language of instruction?
The live sessions run in the language listed under "Available Languages" above. Slides and code files are typically available in all four (English, Russian, Ukrainian, Georgian). Many students attend sessions in one language and read materials in another — that's normal.
Will I be ready for a real job after this course?
One course rarely gets anyone hired by itself — for any field. What this course gives you is the foundation a junior hiring manager expects: working code you wrote yourself, the vocabulary to read other people's code, and the practice habits that make you employable. The honest answer to "am I ready?" is on our roadmap (link in the page header) — open the level you want to reach and read the "You're ready when" checklist.
Can I pay in instalments?
Yes for courses with a "Monthly Payment" option in the at-a-glance ribbon — usually two or three equal monthly payments. Courses without that option are paid in one go. If the price is the blocker, write to us — we look at every case.
You may also like
Building Agents with the Claude Agent SDK
Design and ship custom AI agents with the Claude Agent SDK. Build agent loops, define tools, manage memory and sub-agents, evaluate behavior, and deploy multi-agent systems that solve real engineering tasks autonomously.
Building MCP Servers & AI Tool Integrations
Master the Model Context Protocol (MCP) — Anthropic's open standard for connecting AI models to external tools and data. Build custom MCP servers, expose APIs to Claude, and architect next-gen AI integrations.
Design Patterns in C#: From Theory to Practice
Master classic GoF design patterns in C#. Learn how to apply creational, structural and behavioral patterns to build clean, flexible and maintainable systems.