Getting Started with Generative AI: A Beginner's Guide

What is generative AI?

Generative AI is a type of artificial intelligence that creates new content — text, code, images, audio and video — instead of only classifying or predicting. When you ask ChatGPT, Claude or Gemini to draft an email, explain a concept or write Python code, you are using generative AI.

This beginner's guide explains how generative AI works, the key ideas you need to know, what you can build with it, and a practical learning path.

How it works, in plain language

Most text-based generative AI is powered by large language models (LLMs). An LLM is a neural network — usually a transformer — trained on a very large amount of text to do one simple thing: predict the next piece of text (a token) given everything before it.

Doing that well, at enormous scale, turns out to produce surprising abilities: summarising, translating, answering questions, following instructions and writing code. After pre-training, models are further tuned on examples of helpful answers and human feedback so they follow instructions and behave more safely.

Key concepts every beginner should know

Tokens and context windows

Models read and write in tokens — chunks of words. The context window is how many tokens a model can consider at once, including your prompt and its answer. It limits how much text you can provide, and token count also drives cost and speed.

Prompts and prompt engineering

A prompt is the instruction you give the model. Good prompts state the task, give context, show an example of the output you want and set constraints such as length or format. Small changes in wording can change results a lot, so testing prompts systematically matters.

Temperature

Temperature controls randomness. Low temperature gives focused, repeatable answers (good for facts and data extraction); higher temperature gives more varied, creative output.

Embeddings and vector search

An embedding turns text into a list of numbers that captures its meaning, so similar ideas end up close together. Storing embeddings in a vector database lets you search by meaning instead of exact keywords.

Retrieval-augmented generation (RAG)

LLMs don't know your company's documents and can state wrong facts confidently (hallucination). RAG fixes much of this: when a question comes in, the system retrieves the most relevant passages from your documents and gives them to the model, which answers using those sources — and can cite them.

Fine-tuning

Fine-tuning trains a model further on your own examples to change its style or improve a narrow task. It is powerful but usually comes after trying good prompts and RAG, which are cheaper and faster.

Tool calling and agents

Modern models can return a structured request to call a function — search the web, query a database, send a message. Combining this with planning produces AI agents that complete multi-step tasks. Read more in What Is Agentic AI?

What you can build

• Document Q&A assistants that answer from PDFs, policies or manuals using RAG
• Customer support helpers that draft replies grounded in a knowledge base
• Data extraction tools that turn invoices, resumes or emails into structured fields
• Coding assistants for internal codebases
• Content tools for summaries, translations and first drafts — with a human review step

Risks to understand from day one

• Hallucinations — always ground factual answers and verify important outputs.
• Privacy — be careful what data you send to external APIs.
• Prompt injection — untrusted text can try to override your instructions; design defensively.
• Cost and latency — long prompts and large models get expensive at scale.
• Evaluation — build a small test set and measure quality before and after every change.

A practical learning path

1. Python basics — you'll call APIs and process text, so comfort with Python is essential.
2. Use LLMs well — learn prompting patterns and structured outputs.
3. Call an LLM API — write a small script that sends prompts and handles responses.
4. Embeddings and RAG — chunk documents, store embeddings, retrieve and answer with citations.
5. Build an app — add a simple backend and user interface.
6. Evaluate and secure it — test quality, handle failures and guard against prompt injection.
7. Move to agents — tool calling, workflows and memory.

Learn generative AI in Hyderabad

Our Generative AI course at The Data School, KPHB covers LLM foundations, prompt engineering, embeddings, vector search, RAG, evaluation and AI app development, ending with a portfolio project such as a RAG document assistant. Join in the classroom in KPHB, Kukatpally or live online — see the Generative AI course in Hyderabad page for batch details, or book a free demo class.

Preparing for interviews? Practise with our free Generative AI interview questions.