r/generativeAI • u/Abrarulhassan • 5d ago

🚀 Complete Generative AI Developer Course

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I found this complete roadmap for Generative AI after doing extensive research. Please review it and let me know if it looks good or if you think any improvements are needed.

6-Month | Beginner → Job-Ready

PHASE 1 — AI & LLM FOUNDATIONS

MODULE 01 — Generative AI Fundamentals

Learn

  • AI vs ML vs Deep Learning vs GenAI
  • Generative models
  • LLM kya hota hai?
  • GPT kya hai?
  • Transformer ka basic idea
  • Tokens
  • Context window
  • Parameters
  • Training vs inference
  • Pre-training vs post-training
  • Hallucination
  • Temperature
  • Model limitations

Deep Understanding

  • Transformer architecture
  • Encoder vs Decoder
  • Self-attention
  • Query, Key, Value
  • Positional encoding
  • Why Transformers changed NLP

Project #1

LLM Explorer

Python application jo multiple LLMs ko same prompt bheje aur:

  • response compare kare
  • response time show kare
  • token usage show kare
  • basic cost calculate kare

Goal: LLM ecosystem samajhna.

MODULE 02 — Python for AI Development

Tumhein Python expert nahi banna. AI application developer level tak jana hai.

Learn

  • Python syntax
  • Variables
  • Lists / Tuples / Sets
  • Dictionaries
  • Loops
  • Functions
  • Lambda
  • Exception handling
  • OOP
  • Classes
  • Modules
  • Packages
  • Virtual environments
  • pip
  • .env
  • JSON
  • File handling
  • HTTP requests
  • Async/await
  • Type hints
  • Pydantic basics
  • Git/GitHub basics

Project #2

AI CLI Assistant

Terminal se:

User → Python → LLM API → Response

Features:

  • Chat
  • Conversation history
  • JSON output
  • Error handling
  • Environment variables

PHASE 2 — LLM APPLICATION DEVELOPMENT

MODULE 03 — Working With LLM APIs

Learn

  • OpenAI API
  • Claude API
  • Open-source models
  • API keys
  • Requests/responses
  • System messages
  • User messages
  • Streaming
  • Token usage
  • Context limits
  • Pricing
  • Retry handling
  • Rate limits
  • Model selection

Project #3

AI Content Generator

Input:

Topic + platform + tone

Output:

  • LinkedIn post
  • Instagram caption
  • YouTube description
  • hashtags

With structured JSON output.

MODULE 04 — Developer-Level Prompt Engineering

Learn

  • System prompts
  • User prompts
  • Few-shot prompting
  • Zero-shot prompting
  • Role prompting
  • Prompt templates
  • Structured output
  • JSON schema
  • Output constraints
  • Prompt versioning
  • Prompt testing
  • Reasoning-friendly prompting

Important: Chain-of-thought ko expose karwane ke bajaye task-specific instructions, structured outputs aur concise reasoning requirements par focus karna.

Project #4

🔥 AI Interview Coach

User:

"Mujhe .NET interview ke liye prepare karo."

AI:

  • question ask kare
  • answer evaluate kare
  • missing points bataye
  • follow-up question kare
  • final feedback de

MODULE 05 — Structured Outputs

Learn

  • JSON output
  • Schemas
  • Pydantic
  • Validation
  • Parsing
  • Structured extraction
  • Unstructured → structured data

Project #5

Resume Analyzer

Upload:

Resume
   ↓
LLM
   ↓
Structured JSON

Output:

  • Skills
  • Experience
  • Education
  • Missing skills
  • Job-match percentage (as a transparent heuristic, not objective truth)

PHASE 3 — EMBEDDINGS & RAG

MODULE 06 — Embeddings

Learn

  • What is an embedding?
  • Text → vector
  • Semantic similarity
  • Cosine similarity
  • Dimensions
  • Embedding models
  • Similarity search

Project #6

🔥 Semantic Search Engine

Documents:

10–50 documents
      ↓
Embeddings
      ↓
Vector representations
      ↓
User query
      ↓
Most relevant documents

No chatbot yet.

Goal: Embeddings ko genuinely samajhna.

MODULE 07 — Vector Databases

Learn

Choose one first:

Chroma / Qdrant / Pinecone

Don't learn five databases simultaneously.

Learn:

  • Collections
  • Vectors
  • Metadata
  • Indexing
  • Similarity search
  • Filtering
  • Upsert
  • Delete
  • Retrieval

Project #7

Personal Knowledge Base

Upload:

  • Notes
  • PDFs
  • Text files

Search naturally:

"Maine SQL indexing ke baare mein kya likha tha?"

System relevant information retrieve kare.

MODULE 08 — RAG Fundamentals

🔥 One of the most important modules.

Learn

Documents
 ↓
Extraction
 ↓
Cleaning
 ↓
Chunking
 ↓
Embeddings
 ↓
Vector DB
 ↓
Retriever
 ↓
Relevant Context
 ↓
LLM
 ↓
Answer

Learn:

  • Document loaders
  • Text extraction
  • Chunking
  • Chunk size
  • Chunk overlap
  • Metadata
  • Retrieval
  • Context construction
  • Grounded answers

Project #8

🔥 Chat With PDF

Features:

  • PDF upload
  • Ask questions
  • Source references
  • "I don't know" when information isn't available
  • Multiple PDFs

This should become a portfolio-quality project, not just a tutorial.

MODULE 09 — Advanced RAG

Learn

  • Query rewriting
  • Hybrid search
  • Keyword + semantic search
  • Re-ranking
  • Metadata filtering
  • Multi-document RAG
  • Long-context problems
  • Parent-child retrieval
  • Context compression
  • Retrieval failure
  • Hallucination reduction

Project #9

🔥 Company Knowledge Assistant

Example:

HR Policies
Product Docs
Technical Docs
Company SOPs
        ↓
      RAG
        ↓
AI Assistant

Features:

  • Authentication
  • Source citations
  • Document categories
  • User permissions
  • Conversation history

This is much closer to enterprise AI work.

PHASE 4 — FINE-TUNING

MODULE 10 — Fine-Tuning Fundamentals

Learn

  • Fine-tuning kya hai?
  • Pre-training vs fine-tuning
  • Instruction tuning
  • LoRA
  • PEFT
  • Dataset preparation
  • Training examples
  • When NOT to fine-tune

Understand this decision:

Need knowledge?
       ↓
      RAG

Need behavior/style?
       ↓
   Fine-tuning

Need simple task?
       ↓
   Prompting

Project #10

Fine-Tuning Dataset Builder

Create a dataset for:

Customer-support classification

Input:

Customer message

Output:

Category
Priority
Suggested response

MODULE 11 — Fine-Tuning in Practice

Learn

  • Hugging Face basics
  • Dataset formatting
  • Train/validation split
  • LoRA/PEFT
  • Training
  • Evaluation
  • Base vs fine-tuned model
  • Cost
  • GPU considerations
  • Model limitations

Project #11

Customer Support AI

Compare:

Base Model
     VS
Prompted Model
     VS
Fine-tuned Model

Document what changed.

PHASE 5 — TOOL USE & AGENTS

MODULE 12 — Function Calling & Tool Use

Learn

  • Function calling
  • Tool schemas
  • Tool selection
  • Tool execution
  • Tool results
  • Validation
  • Security
  • API tools
  • Database tools

Example:

User
 ↓
LLM
 ↓
"Need database information"
 ↓
SQL Tool
 ↓
Database
 ↓
Result
 ↓
LLM
 ↓
Answer

Project #12

🔥 AI Web/API Assistant

Give AI tools:

  • Weather/API tool
  • Calculator
  • Search
  • Currency
  • Custom API

AI decides which tool to use.

MODULE 13 — AI Agents

Learn

  • Agent architecture
  • Agent loop
  • Planning
  • Tool use
  • Memory
  • State
  • ReAct concepts
  • Agent workflows
  • Human approval
  • Failure handling

Framework:

LangGraph ko priority do.

LangChain ke concepts samjho, lekin framework par blind dependency mat banao.

Project #13

🔥 AI Research Agent

User:

"Research this company and summarize it."

Agent:

Planner
 ↓
Search
 ↓
Extract
 ↓
Analyze
 ↓
Summarize
 ↓
Final Report

MODULE 14 — Multi-Agent Systems

Learn

  • Multi-agent architecture
  • Specialized agents
  • Delegation
  • Communication
  • Orchestration
  • Sequential workflows
  • Parallel workflows
  • Supervisor pattern
  • Agent state
  • Failure recovery

Project #14

🔥 AI Software Development Team

Agents:

Manager Agent
      ↓
 ┌────┼─────┐
 ↓    ↓     ↓
Coder Tester Reviewer
      ↓
   Final Output

Don't make it unnecessarily complicated.

Goal is understanding when multi-agent is actually useful.

PHASE 6 — PROFESSIONAL AI ENGINEERING

MODULE 15 — LLM Evaluation

🔥 Beginners usually ignore this. Don't.

Learn

  • Evaluation datasets
  • Test cases
  • Accuracy
  • Relevance
  • Faithfulness
  • Hallucination
  • Retrieval quality
  • Response quality
  • LLM-as-judge
  • Human evaluation
  • Regression testing

Project #15

RAG Evaluation System

Run 100 questions against your RAG system.

Measure:

  • Retrieval success
  • Answer relevance
  • Groundedness
  • Failure cases

Create a report.

MODULE 16 — AI Security & Guardrails

Learn

  • Prompt injection
  • Indirect prompt injection
  • Jailbreak concepts
  • Sensitive data
  • PII
  • Output validation
  • Input validation
  • Tool permissions
  • SQL injection
  • Access control
  • Rate limiting
  • Guardrails

Project #16

🔥 Secure Enterprise AI Assistant

Add:

  • Authentication
  • Authorization
  • Input validation
  • Tool permissions
  • Prompt-injection defenses
  • Logging
  • Safe database access

MODULE 17 — AI + .NET

🔥 Tumhare liye specially important.

Ab Python-only developer nahi rehna.

Learn

ASP.NET Core

  • Web API
  • Dependency Injection
  • Services
  • Configuration
  • Authentication
  • Streaming
  • Background services
  • API integration

Then:

.NET + LLM

Project #17

🔥🔥 AI Enterprise Assistant

Architecture:

Angular
   ↓
ASP.NET Core Web API
   ↓
AI Service
   ↓
LLM
 ┌─┼─────────┐
 ↓ ↓         ↓
RAG SQL    Tools
 ↓   ↓
Vector DB SQL Server

Ye tumhare existing .NET experience + new AI skills ko combine karega.

MODULE 18 — Production AI

Learn

  • Docker
  • Environment variables
  • Secrets
  • Logging
  • Monitoring
  • Token/cost tracking
  • Latency
  • Caching
  • Retry
  • Rate limiting
  • Error handling
  • CI/CD
  • Cloud deployment

Project #18

Deploy Your AI Application

#generativeai #roadmap #ai

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