Overview: Generative AI Course in Centre-ville

Built for Centre-ville builders

Every module ends with something working — a RAG pipeline, an agent, an eval suite — built live alongside peers from Centre-ville and beyond. This goes past prompting into the actual engineering behind production GenAI.

Curriculum breakdown

Module 1: Prompt engineering and structured chains

Centre-ville students learn structured prompt design and function calling, not just one-off prompt tricks.

Topics covered

• Prompt architecture and function calling
• Structured output validation
• Multi-step prompt chains

Module 2: RAG pipelines with vector databases

Building a working retrieval-augmented generation pipeline, end to end, as a Centre-ville student.

Topics covered

• Chunking and ingestion strategy
• Vector database selection and indexing
• Retrieval quality evaluation

Module 3: Agents and tool use

Building an agent that uses tools reliably, for Centre-ville students moving beyond single-turn prompting.

Module 4: Evals, guardrails and deployment

Shipping what you've built with monitoring and safety guardrails in place.

How the cohort runs

Format and schedule

Live, project-based cohort

Fully online, live over Zoom, for Centre-ville learners and peers across every module.

Who this course is for

Engineers and builders

Centre-ville engineers who want the architecture behind production GenAI, not just API basics.

Technical founders

Centre-ville founders building an LLM-powered product who need to move past a fragile prototype.

Outcomes and support

Portfolio project

Centre-ville students leave with a working RAG or agent project they can show employers.

Prerequisites

Basic Python recommended

The course focuses on architecture decisions for Centre-ville students, not syntax fundamentals.

Note

Covers OpenAI, Anthropic (Claude), and open-source models, so Centre-ville students aren't locked into one provider's mental model.

Quick answer

The Generative AI Course in Centre-ville is a hands-on, project-based program covering prompt engineering, RAG pipelines, agents and evals — the engineering behind production LLM systems, not just prompting tricks. It's built for Centre-ville engineers and technical founders who want to move a fragile prototype into something production-ready, and it's worth it if your goal is a working portfolio project, not just theoretical understanding of how LLMs work.

How this compares to free YouTube tutorials or API documentation

• You build a working RAG pipeline, agent and eval suite during the cohort, not just watch someone else build one
• Covers OpenAI, Anthropic (Claude) and open-source models together, so Centre-ville students aren't locked into one provider's documentation and mental model
• Live feedback on your architecture decisions from a practitioner shipping production GenAI systems, not a comments section
• Structured progression from prompting through RAG, agents, evals and deployment, instead of scattered standalone tutorials

Why Centre-ville builders choose Deepak Suhag

Taught by someone who ships production GenAI systems for clients — not a course built from documentation alone.