Generative AI
Build applications powered by large language models — from prompt engineering to production RAG pipelines.
Start Learning Generative AIWhat You'll Learn
- What Large Language Models (LLMs) are and how they work
- How to write effective prompts (prompt engineering)
- How to call LLM APIs with Python using the OpenAI SDK and LangChain
- What Retrieval-Augmented Generation (RAG) is and how to build one
- How to use embeddings to search through documents with vector databases
- How to chain prompts and build multi-step AI pipelines
- How to deploy a simple GenAI application
Introduction to Generative AI
Generative AI refers to AI systems that can create new content — text, code, images, and more — by learning patterns from huge amounts of data. The most popular examples are ChatGPT, Claude, and Gemini, all powered by Large Language Models (LLMs). These models are trained on billions of text examples and can write essays, generate code, answer questions, and summarize documents.
At its core, an LLM takes a text prompt as input and predicts the most likely next tokens (words or parts of words) to generate a response. The model does not "know" facts — it learns statistical patterns. This is why the quality of your prompt directly affects the quality of the output, a skill called prompt engineering.
In 2025, Generative AI is no longer just about chatting with an AI. Engineers are building real-world systems like Retrieval-Augmented Generation (RAG) — where an LLM is connected to your own documents or database so it can answer questions about your specific data. Understanding GenAI is now a core skill for data engineers and software developers alike.
Video Tutorials
Handpicked free YouTube videos to accelerate your understanding
Intro to Large Language Models
A technical yet beginner-accessible walkthrough of how LLMs work — training, emergent abilities, RLHF, and what comes next for AI.
Introduction to Generative AI
Google's official beginner course — what Generative AI is, how it differs from traditional ML, and real-world applications.
Your first LLM call with LangChain (beginner-friendly)
Copy the code below and paste it into your Python environment or our free online compiler.
# Install: pip install langchain langchain-openai
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, SystemMessage
# 1. Create the model (replace with your API key)
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
# 2. Write a prompt with a system role and a user question
messages = [
SystemMessage(content="You are a helpful data engineering tutor."),
HumanMessage(content="Explain what Apache Spark is in 2 sentences.")
]
# 3. Call the model and get a response
response = llm.invoke(messages)
print(response.content)
# Output:
# Apache Spark is an open-source distributed computing framework
# designed for fast processing of large-scale data across a cluster.
# It supports batch processing, streaming, SQL, machine learning,
# and graph processing through a unified API.Key Concepts Explained
Master these terms and you'll understand 80% of the conversations in this field.
LLM (Large Language Model)
A neural network trained on massive text data (billions of words) that can generate, summarize, translate, and answer questions in natural language. Examples: GPT-4, Claude, Llama 3.
Prompt Engineering
The practice of designing the input text (prompt) you send to an LLM to get the best possible output. A well-structured prompt gives context, specifies format, and provides examples.
Token
The smallest unit an LLM processes — roughly 0.75 words. "Generative AI" is 3 tokens. LLMs have a "context window" limit (e.g. 128K tokens) on how much text they can process at once.
Embeddings
A way to convert text into a list of numbers (a vector) that captures meaning. Similar sentences have similar numbers. Used to search documents semantically instead of by exact keywords.
RAG (Retrieval-Augmented Generation)
A pattern where you first retrieve relevant chunks from your own documents (using embeddings search), then pass those chunks to an LLM as context, so it answers from your data.
Vector Database
A database optimised for storing and searching embeddings. When you have thousands of documents, a vector database (like Chroma, Pinecone, or Weaviate) finds the most relevant ones fast.
Temperature
A parameter (0.0 to 1.0+) that controls how creative/random the LLM response is. 0 = deterministic, factual answers. 1.0 = creative, varied responses.
LangChain
A Python framework that makes it easy to build applications with LLMs. It provides tools for chaining prompts, connecting to databases, building agents, and managing memory.
Your Generative AI Learning Path
Follow these steps in order — each one builds on the last. Designed for complete beginners.
- 1
Python Basics
Learn Python functions, dictionaries, lists, and installing packages with pip. This is the foundation for all AI work.
- 2
APIs & HTTP
Understand what an API is, how to make HTTP requests in Python using the requests library, and how to read JSON responses.
- 3
Prompt Engineering
Learn to write clear, structured prompts. Practice zero-shot, few-shot, and chain-of-thought prompting techniques.
- 4
LangChain & LLM APIs
Use LangChain or the OpenAI SDK to call models, build prompt templates, and create simple chains.
- 5
Embeddings & Vector Search
Generate embeddings with sentence-transformers or OpenAI. Store and search them in ChromaDB or FAISS.
- 6
Build a RAG Application
Combine a document loader, embeddings, vector store, and LLM to answer questions from your own PDF or website.
- 7
AI Agents
Build agents that can use tools (search, calculator, code runner) to solve multi-step problems autonomously.
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