GenAI Full-Stack Developer Track

A structured learning path from Python fundamentals to production GenAI applications — covering REST APIs, RAG, and multi-agent systems with Next.js + FastAPI.

Every phase includes theory, hands-on practice, and a runnable project. Complete phases in order; Phases 1–2 are required before the GenAI capstone projects.

Stack: Python · FastAPI · Next.js · OpenAI / Ollama · Estimated time: ~75–110 hours

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What you'll learn

Outcome	Covered in
Python syntax, OOP, files, testing	Phase 1
Production REST APIs — auth, DB, Docker	Phase 2
RAG — chunking, embeddings, retrieval, citations	Phase 3
Multi-agent orchestration — triage, tools, escalation	Phase 4
Full-stack GenAI apps (Next.js UI + FastAPI backend)	Phases 3–4

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Learning path

Phase	Folder	Project / content	Time
1	core-python/	7-module curriculum + interview prep	~40–60 hrs
2	backend-with-fastapi/	Task Management API (auth, RBAC, DB)	~10–12 hrs
3	fullstack-genai/rag-assistant/	RAG knowledge assistant	~15–25 hrs
4	fullstack-genai/support-agent/	Multi-agent customer support	~10–15 hrs

core-python  →  backend-with-fastapi  →  rag-assistant  →  support-agent
  (Python)         (FastAPI API)          (RAG + UI)         (Agents + UI)

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Repository structure

python-learning/
├── core-python/
│   ├── modules/                 # 01 → 07 (THEORY + PRACTICE in each .py file)
│   └── interview-prep/          # Master guide + 195+ Q&A
├── backend-with-fastapi/
│   ├── LEARNING-GUIDE.md        # 12-step curriculum
│   ├── docs/                    # REST, auth, DB, testing, Docker
│   └── task-api/                # FastAPI project
└── fullstack-genai/
    ├── rag-assistant/           # Phase 3 — LEARNING-GUIDE + docs + app
    └── support-agent/           # Phase 4 — LEARNING-GUIDE + docs + app

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Quick start

Verify Python and run your first lesson:

python3 core-python/modules/01-fundamentals/01_verify_installation.py
python3 core-python/modules/01-fundamentals/04_hello_world.py

Each lesson file: read the THEORY docstring at the top, then run the file for PRACTICE sections.

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Phase 1 — Core Python

Folder: core-python/ · Guide: README

#	Module	Topics
01	fundamentals	Setup, syntax, types, control flow
02	data-structures	List, tuple, set, dict
03	functions-and-modules	Functions, imports, packages
04	object-oriented-programming	Classes, inheritance, OOP
05	data-handling	Strings, files, exceptions, logging
06	intermediate-python	Iterators, decorators, types, stdlib
07	advanced-and-production	Testing, concurrency, performance

Interview prep: core-python/interview-prep/ + per-module INTERVIEW.md

Before Phase 2: complete modules 01, 03, 04, 05.

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Phase 2 — Backend with FastAPI

Folder: backend-with-fastapi/ · Guide: LEARNING-GUIDE.md

Production-style Task Management REST API — JWT auth, RBAC, SQLAlchemy, Alembic, rate limiting, pytest, Docker. Same patterns used in the GenAI projects.

Resource	Description
Learning Guide	12-step curriculum
docs/	Concept guides (REST → deployment)
task-api/	Project reference

cd backend-with-fastapi/task-api
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt && cp .env.example .env
make dev

API docs: http://127.0.0.1:8000/docs

Email	Password	Role
admin@example.com	admin123	admin
user@example.com	user123	user

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Phase 3 — RAG Assistant

Folder: fullstack-genai/rag-assistant/ · Guide: LEARNING-GUIDE.md

Upload documents → chunk & embed → vector search → chat with grounded answers and source citations. Includes RAG vs plain LLM compare mode.

Resource	Description
Learning Guide	RAG theory + build steps
docs/	Chunking, embeddings, LLM selection
README	Features, API, structure

cd fullstack-genai/rag-assistant
cp backend/.env.example backend/.env   # add OPENAI_API_KEY
make install && make dev

Chat UI	API
http://localhost:3000	http://127.0.0.1:8000/docs

Prerequisites: Phase 2 + OpenAI API key (or Ollama).

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Phase 4 — Support Agent

Folder: fullstack-genai/support-agent/ · Guide: LEARNING-GUIDE.md

Multi-agent customer support — triage → knowledge / orders / escalation agents → supervisor. UI shows full agent trace on every reply.

Agent	Role
Triage	Classify intent (returns, billing, order status, …)
Knowledge	Answer from support KB (RAG-lite)
Orders	Look up mock order data
Escalation	Create human handoff tickets
Supervisor	Route + polish final response

cd fullstack-genai/support-agent
cp backend/.env.example backend/.env   # add OPENAI_API_KEY
make install && make dev

Support UI	API
http://localhost:3001	http://127.0.0.1:8001/docs

Prerequisites: Phase 3. Runs on separate ports so it can run alongside rag-assistant.

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Skills matrix

Skill	P1	P2	P3	P4
Python & OOP	✓
REST APIs & HTTP		✓	✓	✓
Auth, DB, pytest, Docker		✓
Embeddings & vector retrieval			✓	✓
LLM prompting & grounding			✓	✓
RAG pipelines			✓	✓
Multi-agent orchestration				✓
Next.js + FastAPI full stack			✓	✓

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License

MIT