Originally published on Builder AWS by David Kljajo
I explored Generative AI by building real projects instead of just reading tutorials. Using Kiro, I experimented, iterated, and learned how to turn ideas into interactive outputs. This story shares my process, lessons learned, and my personal journey with screenshots along the way.
🚀 My Builder Journey into Generative AI
I didn’t start with a clear plan — I just knew I wanted to understand how Generative AI actually works beyond the hype.
At first, things were confusing. There were too many tools, too many tutorials, and a lot of theory that didn’t translate into real skills. Instead of trying to learn everything, I focused on building something small and practical. That’s when I started using Kiro.
🛠️ What I Built
Using Kiro, I explored how to:
- Ask better prompts
- Structure ideas into usable outputs
- Turn concepts into something interactive
I experimented with different approaches, tested what worked, and improved step by step. It wasn’t perfect, but it was real progress.
📸 My Process (Screenshots)
🧠 Questions I Asked Kiro
Kiro Answer 1 (Career-Focused)
Question: What is the most practical GenAI certification path in 2026 if I want to build real-world projects and not just pass exams?
Answer from Kiro:
- Foundations (free): DeepLearning.AI, LangChain Academy
- Pick 1 Cloud Cert: AWS AI Practitioner, Google GenAI Engineer, or Azure AI-102
- 3 Real Projects:
- RAG chatbot
- AI agent with tools
- Full-stack AI product
- Showcase: GitHub + live demos — projects > certs
Kiro Answer 2 (Learning + Strategy)
Question: Design a step-by-step roadmap to become certified in Generative AI, including free resources, hands-on projects, and how to stand out to employers.
Answer from Kiro:
- Weeks 1-2: Learn AI basics & GenAI landscape
- Weeks 3-6: Core stack: Python, LangChain, OpenAI API
- Weeks 5-8: Build RAG chatbot
- Weeks 8-14: Pass cloud cert
- Weeks 12-20: Build AI agent + full-stack AI product
- Ongoing: Post projects, contribute to open source, polish GitHub & LinkedIn
🧩 The Core Philosophy
Certifications alone won't get you hired or make you effective. The winning combo is: free/cheap foundational certs + cloud cert for credibility + a portfolio of 3 deployed projects.
Phase 1 — Foundation (1-2 months, mostly free)
- DeepLearning.AI short courses — prompt engineering, LangChain, RAG, agents. Free, practical, 1-2 hours each
- LangChain Academy — free, hands-on agent building
- Fast.ai Practical Deep Learning — model-level understanding
- Tools to get comfortable with: Python, LangChain/LangGraph, OpenAI/Anthropic APIs, vector DBs (Chroma, Pinecone, Weaviate), HuggingFace
Phase 2 — One Cloud Cert (pick based on your target job)
| Target | Cert |
|---|---|
| AWS shops | AWS AI Practitioner → AWS ML Specialty |
| Google/GCP | Google Cloud GenAI Engineer |
| Microsoft/enterprise | Azure AI Engineer Associate (AI-102) |
| Vendor-neutral | IBM AI Engineering Professional (Coursera) |
Phase 3 — The 3 Portfolio Projects That Actually Matter
- RAG chatbot — ingest PDFs/docs, answer questions, deploy using LangChain + Chroma + frontend (Streamlit/FastAPI)
- AI agent with tools — searches web, runs code, calls APIs (LangGraph/CrewAI)
- Full-stack AI product — real use case, auth, deployed URL (AI assistant, code reviewer, domain Q&A)
Each project should have a GitHub repo, live demo, and write-up.
What to Skip
- Generic "AI for everyone" certs with no coding
- Paying for bootcamps before trying free resources
- Chasing every framework — pick one (LangChain or LlamaIndex) and go deep
Realistic Timeline
| Months | Focus |
|---|---|
| 1-2 | DeepLearning.AI courses + first RAG project |
| 3-4 | Cloud cert prep + agent project |
| 5-6 | Full-stack project + polish portfolio + apply |
Stage-by-Stage Guide
Stage 1 — Get Your Bearings (Weeks 1-2, free)
- 3Blue1Brown Neural Networks series
- DeepLearning.AI: Generative AI for Everyone
- Read: “Attention Is All You Need” abstract + plain-English explainer
Stage 2 — Learn the Core Stack (Weeks 3-6, free)
- DeepLearning.AI short courses
- LangChain Academy — Introduction to LangGraph
- HuggingFace free courses — NLP + Agents
- Tools: Python, LangChain, OpenAI/Anthropic SDK, Chroma/FAISS, Streamlit, FastAPI, Docker basics
Stage 3 — First Real Project (Weeks 5-8)
- Build a RAG chatbot with minimal stack: LangChain + Chroma + OpenAI API + Streamlit + deployed (Railway/Render free tier). Push to GitHub with README.
Stage 4 — Pick One Cloud Cert (Weeks 8-14)
- AWS-heavy companies → AWS Certified AI Practitioner → ML Specialty
- Google/GCP roles → Google Cloud GenAI Engineer
- Enterprise/Microsoft → Azure AI Engineer Associate (AI-102)
- Vendor-neutral → IBM AI Engineering Professional (Coursera)
Stage 5 — Build Remaining Portfolio Projects (Weeks 12-20)
- Project 2 — AI Agent with Tools (multi-step tasks, research agent, uses LangGraph/CrewAI)
- Project 3 — Full-stack AI Product (real use case, auth, live URL, FastAPI + React/Next.js + Supabase + Vercel/Railway)
Stage 6 — Stand Out to Employers (Ongoing)
- LinkedIn posts, dev.to/Hashnode articles for projects
- GitHub: pinned repos, clean READMEs, live demos
- Open source contributions
- Resume: projects > certifications, quantify impact
Certs get you past ATS filters; your portfolio gets the interview; your ability to explain projects gets the offer.
Originally published on Builder AWS
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