1. What is Generative AI?
🇬🇧 English Definition
Generative AI is a class of artificial intelligence systems that learn patterns from training data and generate brand new content such as text, code, images, audio, video, and structured data.
🇮🇳 Hinglish Definition
Simple language mein: traditional software rules follow karta hai, jabki GenAI learned patterns ke basis par new original content generate karta hai. Chatbots, image generators aur coding assistants iske major examples hain.
// Basic Conceptual Representation of GenAI Inference
def generate_content(user_prompt, model_parameters):
// 1. Process and tokenize user input prompt
tokens = tokenize(user_prompt)
// 2. Pass tokens through neural network layers
predictions = model_parameters.forward(tokens)
// 3. Decode output token probabilities into text
output_text = decode(predictions)
return output_text
print(generate_content("Explain gravity", model_params=None))
🔍 Line-by-Line Code Breakdown
- def generate_content(user_prompt, model_parameters): Defines function taking prompt and model weight parameters.
- tokens = tokenize(user_prompt): Converts raw text into numerical token representation.
- predictions = model_parameters.forward(tokens): Computes probabilistic next token distribution.
- output_text = decode(predictions): Converts numeric output IDs back into readable natural language string.
🎯 Interview Focus & Questions
Q: What differentiates Generative AI from Discriminative AI?
A: Discriminative AI predicts labels or decisions given input data (P(Y|X)), whereas Generative AI models the underlying data distribution (P(X) or P(X,Y)) to produce whole new data samples.
22. RAG — Retrieval-Augmented Generation
🇬🇧 English Definition
Retrieval-Augmented Generation (RAG) retrieves relevant external facts from a knowledge index (vector store/database) and injects them into the prompt context so the LLM grounds its answer in factual domain data.
🇮🇳 Hinglish Definition
RAG architecture enterprise documents (PDFs, policies, DBs) ko embeddings banana aur user query ke time exact matching chunks retrieve karke prompt context mein bhejta hai taaki model accurately facts bol sake.
// Basic RAG Workflow Simulation in Python
def simple_rag(query, vector_db, llm_client):
// 1. Convert user query into dense vector
query_vector = embed(query)
// 2. Retrieve top-k relevant document chunks from Vector DB
retrieved_chunks = vector_db.search(query_vector, top_k=3)
// 3. Construct grounded prompt context
context_str = "\n".join(retrieved_chunks)
prompt = f"Context:\n{context_str}\n\nQuestion: {query}\nAnswer based ONLY on context:"
// 4. Generate grounded completion
return llm_client.generate(prompt)
print("RAG Pipeline Ready.")
🔍 Line-by-Line Code Breakdown
- vector_db.search(query_vector, top_k=3): Performs semantic search returning top matching text chunks.
- prompt construction: Bounds model answer explicitly within retrieved text context.
🎯 Interview Focus & Questions
Q: Why choose RAG over Fine-Tuning for real-time document Q&A?
A: RAG allows instant knowledge updates without expensive GPU retraining, provides source attribution (citations), and guarantees strict permission access control.
33. AI Agents
🇬🇧 English Definition
An AI Agent is an autonomous software system powered by an LLM that receives a high-level goal, plans sub-tasks, executes external tools, evaluates results, and iterates until the goal is accomplished.
🇮🇳 Hinglish Definition
Chatbot sirf simple reply karta hai. AI Agent ko ek goal milta hai, wo khud multi-step plan banata hai, tools (search, DB, calculator) execute karta hai aur results observe karke final goal hit karta hai.
// Basic ReAct (Reasoning + Acting) Agent Loop Simulation
def agent_react_loop(goal, max_steps=3):
state = f"Goal: {goal}"
for step in range(1, max_steps + 1):
print(f"--- Step {step} ---")
// 1. Reason
thought = f"Thought {step}: I need to check data for {goal}"
// 2. Act
action = f"Action {step}: Call tool search_database('{goal}')"
// 3. Observe
observation = f"Observation {step}: Found valid record matching {goal}"
print(thought)
print(action)
print(observation)
return "Final Answer: Goal achieved successfully."
print(agent_react_loop("Customer refund validation"))
🔍 Line-by-Line Code Breakdown
- agent_react_loop: Implements the classic ReAct framework loop: Thought -> Action -> Observation cycle.
🎯 Interview Focus & Questions
Q: What is the ReAct framework in AI Agents?
A: ReAct (Reasoning + Acting) combines step-by-step verbal reasoning traces with action tool calls, allowing the agent to update its plan based on environment feedback.
57. 90-Day Generative AI Learning Roadmap
🇬🇧 English Definition
Structured 90-day learning curriculum: Days 1-10 (Python & APIs), Days 11-20 (Neural Nets & Transformers), Days 21-30 (Prompting & APIs), Days 31-45 (RAG & Vector DBs), Days 46-55 (FastAPI & Agents), Days 56-65 (Multimodal & Open Source), Days 66-75 (Fine-tuning & LoRA), Days 76-82 (Security & Eval), Days 83-90 (Capstone Project Deployment).
🇮🇳 Hinglish Definition
Day-by-Day 90-Day Study Plan: Python → AI/ML Basics → LLM Prompting → RAG & Vector DBs → Agents & FastAPI → Multimodal AI → Fine-tuning & LoRA → Security & MLOps → Capstone Project.
🎯 90-Day Learning Master Plan
Days 01-10: Python, JSON, Async HTTP & Git Fundamentals
Days 11-20: AI/ML Basics, Tokens, Embeddings & Transformers
Days 21-30: LLM Prompt Engineering, Decoding & SDK APIs
Days 31-45: RAG Architecture, Chunking, Vector DBs & Retrieval
Days 46-55: AI Agents, Tool Calling, FastAPI & Memory
Days 56-65: Multimodal AI, Audio/Vision & Open Source Models
Days 66-75: Fine-Tuning, LoRA/PEFT & Model Quantization
Days 76-90: Security, Guardrails, LLMOps & Production Deployment