Build one practical RAG application and one Agentic AI workflow through guided live sessions — then package them with GitHub repositories, architecture diagrams, polished READMEs, resume bullets, LinkedIn stories, and clear project explanations.
Guided live building. Practical implementation. Professional portfolio packaging.
You've watched the videos and understood the concepts — but your GitHub still doesn't reflect your AI learning.
Most beginner projects look identical. You need projects built around a real use case and a clear architecture.
The code runs, but when someone asks why you chose the architecture, your explanation becomes difficult.
Repositories, notebooks, experiments — but nothing packaged into something you feel confident sharing.
But you don't yet have tangible proof that connects those concepts to something you built.
Knowledge becomes far more valuable when you turn it into proof.
This accelerator deliberately does not hand you ten superficial projects. Instead, you build two focused projects — and learn to understand, structure, explain, and package them professionally.
You won't just leave with code.
You'll leave knowing how to talk about what you built.
Two projects. Two stories.
One much stronger AI portfolio.
Recommended default: an AI Knowledge Assistant or Document Intelligence Assistant — grounded in a use case that actually means something on your resume.
Choose from an AI Research Assistant, a Customer Request Resolution Workflow, an AI Operations Assistant, or a Multi-Step Business Workflow Assistant.
You won't receive a project title on Day 1 and be told to "go build it." Each project is developed through guided live sessions — with direction at every step.
Turn a vague project idea into a structured, defensible use case.
Create your Project 1 brief.
Project Brief + Architecture Canvas
"I know exactly what I am building and why."
Your first working portfolio project, built end to end with guidance.
Build Project 1.
Working Project 1 + GitHub Repository
"I have my first working portfolio project."
Move from "it runs" to "I can present this to anyone."
Package Project 1.
Portfolio Pack #1
"I can explain this project, not just run it."
A second project that shows a different class of AI capability.
Build Project 2.
Working Agentic AI Project + GitHub Repository
"I now have a second project that demonstrates a different AI capability."
The week that turns a project you built into a project you can discuss with confidence.
You'll learn the next questions experienced engineers ask — the ones that turn a good project conversation into a senior one.
Document your decisions and trade-offs.
Technical Trade-Off Notes + Portfolio Pack #2
"I can discuss why I built it this way."
Everything you've built becomes something you can send in a single link.
Repository naming · README structure · screenshots · architecture · setup · project explanation
Strong project bullets · architecture + responsibility + outcome · no buzzword stuffing
Business problem · solution · architecture · learning · project link
Assemble your complete portfolio.
Final AI Portfolio Kit
"I now have two AI projects I can confidently show and explain."
This is not another folder of course notes.
This is your AI proof-of-work portfolio.
The goal isn't more code.
The goal is stronger proof of work.
The experience deliberately combines implementation, architecture, project thinking, technical reasoning, team-role awareness, portfolio packaging, and professional communication.
You do not need to become an expert in every infrastructure layer before building a portfolio. You need to understand your project well enough to own the conversation about it — and that is exactly what these six weeks are built around.
You'll know what questions come next — without losing focus on completing your portfolio.
Typical profiles: Software Engineers · Backend Engineers · QA & Automation Engineers · Data Engineers · Data Scientists · Cloud Engineers · DevOps · MLOps Engineers · Technical Leads · Architects moving toward AI
Two completed AI projects — one RAG / Knowledge AI application and one Agentic AI workflow — along with GitHub repositories, architecture diagrams, READMEs, resume bullets, LinkedIn positioning, and project explanation material for each.
No. Coding is part of the experience, but the accelerator also teaches you how to structure, understand, explain, and package your projects professionally — which is where most learners actually get stuck.
Yes. The accelerator is designed around guided building and weekly implementation. You will be guided, but you will build.
Two. The focus is depth, completion, and clarity rather than ten superficial projects you'd struggle to explain.
One practical RAG / Knowledge AI application and one practical Agentic AI workflow — two genuinely different AI capabilities on your portfolio.
Basic Python familiarity is recommended. You should be comfortable reading and modifying simple Python code.
Yes, after completing and personalizing your projects. The accelerator specifically includes portfolio packaging for GitHub, resume, and LinkedIn.
Yes, naturally. You'll learn how to explain the problem, architecture, your responsibility, decisions, challenges, and future improvements — the same structure that makes any project conversation stronger.
Yes. Some weekly project work is expected. The live sessions provide direction and guidance; completing and personalizing the project is part of the learner journey — and it's what makes the final portfolio genuinely yours.
You'll build practical end-to-end portfolio applications and understand their architecture, technical decisions, and improvement paths. We'll also introduce the questions experienced engineers consider as these systems grow — reliability, quality, security, monitoring, cost, and scale.
Yes — every session is recorded and you get lifetime access to all recordings. Rewatch any week, any time, for as long as you need. Miss a Thursday and you can catch up without falling behind.
No. The accelerator strengthens your proof of work and your ability to present your AI projects professionally. It does not provide job guarantees.
In six guided weeks, build two practical AI projects and turn them into a portfolio you can confidently showcase and explain.