Are you an engineer trying to upskill in AI?

This course teaches you to build production AI systems end-to-end.

RAG is the core skill, apart from that you'll also learn the full AI stack: LLM fundamentals, data ingestion, chunking strategies, embedding models, vector databases, hybrid search, reranking, evaluation frameworks (RAGAS, DeepEval, LLM-as-Judge), semantic caching, AI systems API design, AI observability and trace management, Docker deployment, and agentic systems.

These are the exact skills companies are hiring for right now. Every week ends with a hands-on project submission on your own data, graded with detailed technical feedback.

RAG (Retrieval Augmented Generation) is how you make AI work with your own data.

LLMs on their own can't access your private documents, internal databases, or real-time information. They hallucinate when they don't know. RAG solves this by retrieving relevant information from your data sources and feeding it to the model at the time of the query. No retraining needed.

This is the pattern behind every production AI application you use today: customer support bots, code assistants, enterprise knowledge systems, copilots. If you're building AI that needs to be accurate and grounded in real data, you're building RAG.

For a deeper dive, read our guide: The Engineer's Guide to RAG.

RAG is to AI engineering what CRUD is to web development. It's the foundational pattern that production AI applications are built on.

But more importantly, building RAG the right way requires mastering the full set of skills the industry demands: AI system design, retrieval and ranking architectures, evaluation methodology, embedding and vector search, LLM orchestration, AI observability, and production deployment.

These are not RAG-specific skills. They are the core competencies of a production AI engineer. RAG is simply the best vehicle to learn all of them together.

Most courses teach you concepts and give you clean demos. This course makes you build, evaluate, and ship. Four things set it apart.

First, you don't just learn techniques, you build decision frameworks. Every week gives you a methodology for choosing the right approach for your data, not a one-size-fits-all tutorial.

Second, evaluation is a first-class citizen. We dedicate an entire week to building golden datasets, LLM-as-Judge pipelines, and triangulating across independent evaluation methods. This is where 60-80% of real production work happens, and almost no course covers it seriously.

Third, every project is on your own data, your own use case. Not a toy dataset we hand you.

Fourth, your capstone is graded personally with detailed written feedback. Engineers from Cohort 1 used their capstone projects to present to VP-level stakeholders, lead AI initiatives at their companies, and position themselves for AI engineering roles.

This course is built for software engineers with production experience who want to build AI engineering skills. You should be comfortable writing and debugging code. The course uses Python, but most engineers can cross-apply their coding skills from any language. No AI or ML experience is needed.

In Cohort 1, 75% of students had 5+ years of professional experience. Titles included Senior Software Engineer, Staff Engineer, VP Engineering, Director of Engineering, and CTO. They came from companies like Microsoft, Amazon, Adobe, Shopify, Citi, Lockheed Martin, and Autodesk.

Whether you're a backend engineer exploring AI, a tech lead evaluating AI adoption for your team, or a product engineer looking to build intelligent features, this course gives you the production-grade foundation to do it.

No. The course starts from first principles in Week 1: how LLMs work under the hood, pre-training, tokenization, attention mechanisms, and post-training. You build up from there, week by week.

In Cohort 1, 23% of students were complete AI beginners. They shipped the same capstone projects as engineers who had already built LLM-powered applications. The course is designed to serve both ends of the spectrum.

The prerequisite is not AI knowledge. It's engineering maturity: the ability to read documentation, debug systems, and think about tradeoffs. If you've shipped production software, you have what it takes.

The cohort runs for 6 weeks with three layers of learning.

First, the core curriculum: 45+ hours of self-paced recorded content, structured week by week with hands-on labs and project submissions. You watch and build on your own schedule.

Second, weekly live sessions with Shivani for Q&A, debugging, and architecture discussions. These are recorded if you can't attend live.

Third, daily cohort chat support on weekdays where you can get help, share progress, and learn from other engineers working through the same challenges.

Plan for 12-14 hours per week. This is an intensive program. You're building a vast new skill set at a high technical bar. But the self-paced design means you decide when those hours happen. Evenings, weekends, lunch breaks. It fits seamlessly around a full-time job.

You have lifetime access to all course materials, recordings, labs, and future updates. There is no expiry.

The curriculum is designed to be revisited. Many students rewatch videos two or three times to fully internalize concepts before moving on.

Some deliberately slow down to prioritize depth of understanding over pace. The goal is mastery, not a deadline.

The course uses a production-grade, industry-standard stack: Haystack for orchestration, Qdrant for vector search, Gemini API and Voyage AI for LLM and embedding models, Redis for semantic caching, FastAPI for API design, Streamlit for prototyping, Opik for observability and tracing, and RAGAS and DeepEval for evaluation.

Every tool was chosen because production teams actually use it. But more importantly, the course teaches you the principles behind each layer: why you'd pick one vector database over another, when to use hybrid search vs dense retrieval, how to evaluate tradeoffs between latency, cost, and accuracy.

These mental models transfer to any stack your company uses, whether that's LangChain, LlamaIndex, Azure AI, or AWS Bedrock.

Yes. Most companies offer $500-$1,000+ in annual learning and development budgets, and this course falls well within that range.

We provide a completion certificate, a detailed course outline, and any additional documentation your L&D or HR team needs to approve the claim.

If you need help making the case internally, the course covers skills that directly map to production AI engineering roles, one of the fastest growing and most in-demand skill sets in the industry right now.

The industry is hiring production AI engineers at scale. The bottleneck isn't model capability. It's engineers who can build reliable systems around models.

This course gives you the exact skill set that's in demand: AI system design, retrieval architecture, evaluation methodology, LLM orchestration, observability, and production deployment.

You also walk out with a portfolio-ready capstone project built on real data, personally graded. In Cohort 1, engineers used their capstone to present to VP-level stakeholders, lead AI initiatives at their companies, and position themselves for AI engineering roles.

The skills and the project together give you both the vocabulary and the evidence for any AI engineering conversation, whether that's an interview, a promotion case, or a team pitch.