GenAI Seekhne Ke 30 Din Ka Plan (Hinglish)

JobRise Team4 मार्च 20266 min read

GenAI engineer roles 2026 mein 60% growth dekh rahi hai. Salaries ₹15 LPA se ₹80 LPA tak (3 saal exp ke baad).

Aur tum YouTube pe "GenAI tutorial India" search kar rahe ho aur confused ho. Sab Sundar Pichai bann'na chahta hai 1 din mein.

30 din ka realistic plan. Free resources. Indian context. No bullshit.

Pehle yeh samjho#

GenAI engineer ban'na hai? Yeh roles hai:

AI Application Developer (entry): GPT-4 APIs use karke apps banata hai. Salary: ₹8-20 LPA
ML Engineer (GenAI focus): RAG systems, fine-tuning. Salary: ₹15-40 LPA
GenAI Researcher: Novel models, papers. Salary: ₹40-1.5 Cr (PhD level)
Prompt Engineer: Pure prompting expert. Salary: ₹10-25 LPA (declining role)

30 din mein tum #1 (AI Application Developer) ban sakte ho. Yeh practical hai. Real jobs aati hai.

Agla guide email par pao

Naye guides aur fresh jobs, ek chhote email mein. Free.

Pre-requisites#

Yeh hone chahiye pehle:

Python basics (variables, functions, classes, list/dict)
Git/GitHub basics
REST API concept
HTML/JS basic (frontend ke liye)
Curiosity aur 30 din ka commitment

Agar Python nahi aati, pehle 1 week Python karo (freeCodeCamp Python tutorial YouTube).

Hafta 1: GenAI fundamentals + APIs#

Day 1-2: GenAI intro

Watch:

3Blue1Brown "Neural Networks" playlist (YouTube, free)
"Transformers explained" Andrej Karpathy (1 hour video)

Read:

OpenAI docs intro (platform.openai.com/docs)
Anthropic API docs intro (docs.anthropic.com)

Yeh sirf samjhne ke liye hai. Math mein deep dive mat karo.

Day 3-4: First API call

Setup:

OpenAI account banao ($5 free credit usually)
ya OpenRouter (cheaper, multiple models)
ya Groq (free tier with Llama models)

Pehla project: Python script jo ChatGPT API call kare aur jokes generate kare.

from openai import OpenAI

client = OpenAI(api_key="sk-...")

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "user", "content": "Tell me an Indian engineering joke"}
    ]
)
print(response.choices[0].message.content)

Yeh kaam karta to GenAI ka 30% samajh aa gaya.

Day 5-7: Streaming, system prompts, parameters

Concepts:

System prompt vs user prompt
Temperature (0 vs 1)
Max tokens
Streaming responses
Function calling (tools)

Project: Apna chatbot banao. Streamlit use karke.

pip install streamlit openai

Streamlit ka chatbot template GitHub pe free milta hai. Customize karo.

Hafta 2: RAG (Retrieval Augmented Generation)#

Yeh sabse important concept hai for jobs. 80% AI applications RAG-based hai.

Day 8-10: RAG basics

Concept: Tumhare apne documents pe ChatGPT chala'na. Pehle relevant chunks dhundo, fir LLM ko context do.

Components:

Document loader (PDF, text, etc.)
Chunker (split text in pieces)
Embedder (text to vectors)
Vector DB (Pinecone, ChromaDB, Weaviate)
Retriever (query → relevant chunks)
LLM (final answer generate)

Watch:

LangChain YouTube series (free)
"Building RAG applications" by Pinecone (YouTube)

Day 11-14: Build RAG app

Project: PDF chatbot. User PDF upload kare. Questions pucho. Answer mile from PDF only.

Stack:

LangChain (orchestration)
ChromaDB (vector store, local, free)
OpenAI embeddings
GPT-4o-mini (LLM)
Streamlit (UI)

Tutorial: "LangChain PDF chatbot" YouTube pe search karo. 50+ tutorials available.

Yeh project har AI job interview mein puchte hai. Achi quality wala project banao.

Hafta 3: Production patterns#

Day 15-17: Prompt engineering

Beyond basics:

Chain of thought prompting
Few-shot examples
Output formatting (JSON mode)
Prompt chaining
Self-critique loops

Practice: 10 different problems prompt karke solve karo. Resume parsing, code review, content generation, etc.

Day 18-21: Agents aur tools

Concept: LLM ko tools de do (web search, calculator, custom APIs). LLM khud decide karta hai kab kaunsa tool use karna hai.

Frameworks:

LangChain Agents
LangGraph (newer, recommended)
OpenAI Assistants API
AutoGen (Microsoft)

Project: Personal assistant agent. Email parse kare, calendar check kare, summary do.

Hafta 4: Deployment + portfolio#

Day 22-25: Real deployment

Apne projects deploy karo:

Streamlit Cloud (free, easy)
Hugging Face Spaces (free)
Vercel (Next.js apps)
Render (free tier)

Each project ka live URL hona chahiye. Recruiters click karte hai.

Day 26-28: GitHub portfolio

Apna GitHub clean karo:

3-5 quality projects
Each project mein detailed README
Architecture diagram
Tech stack listed
Demo video (1-2 min)
Live link

Top GenAI portfolios mein dekh:

github.com/topics/genai-portfolio (browse karo)
github.com/topics/rag-application

Day 29-30: Job applications

Resume update:

Headline: "GenAI Engineer | LangChain, RAG, LLMs"
5 projects listed
Skills section: Python, LangChain, LangGraph, ChromaDB, OpenAI API, Anthropic API, Streamlit

Apply: