Overview: Generative AI

Built for builders

Every module ends with something working — a RAG pipeline, an agent, an eval suite — not just slides about what LLMs are.

Who this is for

• Engineers who want to add GenAI to their toolkit
• Technical founders building AI features
• Data/ML practitioners moving into applied GenAI

Quick answer

The Generative AI course is a 6-week, hands-on program teaching prompt engineering, RAG pipelines, agents and evals. It's for engineers, technical founders and data/ML practitioners who want to build production GenAI systems, and it ends with a working RAG or agent project.

How this compares to free prompt engineering tutorials

• Free tutorials mostly cover prompting basics; this course goes into RAG architecture, vector databases and agentic tool use that production systems actually need
• Every module ships something working — a pipeline, an agent, an eval suite — instead of slides about how LLMs work
• Evals and guardrails, the part that separates a demo from a production system, are a dedicated module here and rarely covered in free content
• You leave with a working RAG or agent project you can show, not just notes on concepts

What you'll walk away with

• A complete RAG pipeline built end to end — ingestion, vector search and grounded generation
• Production-grade prompt chains and function calling that hold up on edge cases
• An automated evaluation suite so output quality doesn't silently regress
• A working AI agent project for interviews or client work
• Working knowledge of OpenAI, Anthropic and open-source model APIs

What this generative AI course actually covers

This course goes beyond simply "how to use ChatGPT" to cover the underlying mechanics of large language models, practical prompt engineering as a discipline, retrieval-augmented generation architecture, and the evaluation practices needed to build genuinely reliable applications rather than impressive demos that fall apart in production.

Using AI tools vs building AI applications

Participants learn the meaningfully different skill set required to build applications for other users, beyond effective personal use of existing AI tools.

Course structure and progression

Common misconception about prompt engineering

Who this course is for

• Developers wanting to build production-grade LLM-powered applications
• Product-minded engineers exploring generative AI feature opportunities
• Technical professionals wanting to move beyond casual AI tool usage

Cost management as a practical engineering skill

Participants learn practical cost management strategies — caching, prompt optimization, model tier selection — treating cost efficiency as a core engineering skill rather than an afterthought addressed only after an unexpectedly large bill arrives.

Handling hallucination and building user trust

A significant part of the course addresses practical mitigation strategies for hallucination — grounding responses in retrieved facts, confidence signaling — recognizing this as a core reliability challenge rather than an occasional edge case to ignore.

Building a portfolio-worthy GenAI application

The capstone project is designed to serve as a genuine portfolio piece, demonstrating a working application with proper evaluation, not just an impressive demo that a technical interviewer or hiring manager can dismiss as superficial.

Prerequisites before starting this course

Basic programming experience is recommended, ideally with some exposure to APIs, though the course reviews foundational concepts before diving into LLM-specific application building. No prior machine learning theory background is required — the focus is on practical application development, not training models from scratch.

Instructor background and teaching approach

Instructors bring direct experience building production GenAI applications, not just familiarity with API documentation, ensuring the practical pitfalls discussed — cost overruns, hallucination handling, latency issues — reflect genuine production experience.

Format: live sessions vs self-paced learning

The course combines live coding sessions and architecture discussions with self-paced material for foundational LLM concepts, balancing flexibility with the hands-on practice needed to genuinely internalize application-building skills.

Group size and individualized feedback

Cohorts are kept intentionally small to ensure each participant receives detailed code review and architecture feedback on their capstone project, rather than generic feedback disconnected from their specific implementation choices.

Certificate and portfolio value after completion

The certificate is accompanied by the completed application and its evaluation pipeline as tangible portfolio evidence, providing concrete proof of capability that a certificate alone cannot demonstrate to a potential employer.

Pricing and payment options

Tuition is presented up front with no surprise charges, and installment plans are offered so cost isn't a barrier for professionals from a wide range of backgrounds.

Support after course completion

Participants retain access to an alumni community where practical questions encountered while building GenAI applications on the job can be asked long after course completion, providing ongoing value beyond the formal curriculum.

Comparing this course to following online tutorials

Participants consistently report that detailed code review on their own capstone application caught issues that following tutorials alone would never have surfaced.

Handling the pace of model releases in the curriculum

Because model capabilities evolve rapidly, curriculum content is reviewed and updated regularly to reflect current model capabilities and best practices, rather than teaching techniques that have since become outdated.

Group pricing for teams and companies

Companies wanting to upskill multiple engineers simultaneously can benefit from group pricing, with the option to adapt project examples to the company's specific tech stack for added relevance.

Remote vs in-person format availability

The course is available both remotely and in-person, with equivalent content and hands-on coding support quality in both formats, allowing participants to choose based on personal preference and logistical constraints.

How this course differs from a general programming bootcamp

Participants who already have programming experience find this course adds genuinely new, LLM-specific skills rather than repeating general programming concepts they've already mastered.

Confidentiality of capstone project content

Participants can base their capstone project on a real application idea from their own company, with appropriate anonymization, and any confidential business details shared during the course are treated with strict discretion.

Career outcomes after completing this course

Graduates commonly move into GenAI engineering roles, lead LLM feature initiatives within existing engineering teams, or build their own AI-powered products as founders. The capstone application frequently becomes the centerpiece of technical interview conversations for these roles.

Balancing technical ambition with production reliability

A recurring theme throughout the curriculum is finding the pragmatic balance between ambitious application ideas and what can be built reliably given current model capabilities, rather than either dismissing GenAI prematurely or overcommitting to unproven reliability.

Networking and community value beyond the curriculum

Cohort-based learning creates a genuine peer network of other GenAI practitioners navigating similar technical challenges, a community that often continues providing value through job referrals and ongoing informal advice long after the formal course concludes.

Final thought on what makes this course different

Many GenAI resources focus on impressive demos showcasing what's theoretically possible. This course focuses instead on the practical engineering judgment needed to ship reliable applications, a genuinely different and more immediately applicable skill set.

Handling AI ethics and responsible application design

Ethical considerations — bias in outputs, appropriate use cases, transparency with users about AI involvement — are woven throughout the curriculum as practical engineering decisions rather than treated as a separate abstract topic disconnected from day-to-day implementation choices.

Working with cross-functional teams as a GenAI engineer

Participants learn practical approaches for collaborating effectively with product managers and designers, translating between technical constraints and product requirements clearly and without unnecessary jargon.

Can this course help with a specific application I'm already building?

Yes — many participants use their capstone project to work through a real application they're already building at their current company, receiving structured feedback with immediate practical application.

Handling participants with varying programming experience levels

Participants with less programming experience are supported with additional foundational material and pairing opportunities, ensuring they can build genuine hands-on skills without feeling overwhelmed by more experienced peers moving at a faster pace.

Is this course updated to reflect evolving model capabilities?

Yes, reviewed regularly to reflect current model capabilities and best practices, ensuring techniques taught always match what's genuinely effective today rather than outdated approaches.

Real-world case studies used throughout the course

Rather than abstract hypotheticals, the curriculum draws on real (anonymized) case studies of GenAI applications that succeeded and failed in production, giving participants concrete examples of the architecture and evaluation decisions that separated the two outcomes.

Handling technical debt and iteration in GenAI applications

Participants learn practical approaches for managing technical debt specific to GenAI applications — outdated prompts, deprecated model versions — recognizing that these applications require different maintenance patterns than traditional deterministic software.

Is there a minimum experience level required to enroll?

Some prior programming experience is recommended, though the course is designed to accommodate a range of backgrounds from junior developers to experienced engineers exploring GenAI for the first time.

Can this course be adapted to my company's specific tech stack?

Yes — while core concepts remain consistent, examples and the capstone project can be tailored to reflect the specific technology stack participants work with.

Handling the transition from feature-focused to reliability-focused thinking

A recurring shift participants undergo during the course is moving from thinking about GenAI in terms of impressive capabilities to thinking in terms of reliability an application must consistently deliver, a subtle but consequential reframing that shapes every subsequent engineering decision.

Confidentiality and support after the capstone presentation

Feedback continues to be available even after the formal capstone presentation, allowing participants to refine their application further as they prepare it for actual job interviews or open-source portfolio publication.

Is the certificate recognized by employers?

The certificate is designed to demonstrate concrete, verifiable skill via the capstone application portfolio rather than relying on name recognition alone, giving employers tangible evidence of practical ability.

Can I retake modules if I need more practice?

Yes — recorded sessions and materials remain accessible after the course, allowing participants to revisit specific modules for additional practice as needed.