AI & ML Building

Build AI Applications That Actually Work

From AI chatbots to RAG pipelines to autonomous agents — learn to build production AI applications with real LLM APIs, vector databases, and modern frameworks.

Start Building AI See the learning path

What You'll Learn to Build

Not theory. Not demos. Real AI applications you can deploy, show to employers, and build a career on.

AI Chatbots

Build conversational AI that understands context, handles multi-turn conversations, and integrates with your application. Use Claude and GPT APIs with streaming responses.

Multi-turn Streaming Context-aware

RAG Pipelines

Retrieval-Augmented Generation from scratch. Chunk documents, create embeddings, store in vector databases, and query with LLMs for accurate, grounded answers.

Embeddings Vector DB Document Q&A

AI Agents

Build autonomous agents that can plan, use tools, search the web, execute code, and complete multi-step tasks. Implement tool-calling, function execution, and agent loops.

Tool Use Planning Autonomous

LLM API Mastery

Master the Anthropic SDK, OpenAI API, and Google AI SDK. Handle streaming, function calling, structured outputs, vision, and error handling in production.

SDKs Function Calling Vision

Prompt Engineering

Write prompts that actually work. System prompts, few-shot examples, chain-of-thought reasoning, output formatting, and prompt templates for reliable results.

System Prompts Few-shot CoT

Vector Search & Embeddings

Understand how embeddings work, choose the right model, build semantic search, and implement similarity matching for recommendation systems.

Semantic Search Similarity Knowledge Base
Architecture Deep Dive

RAG Pipeline — What You'll Build

Retrieval-Augmented Generation is the #1 AI pattern in production. Here's the complete pipeline you'll implement.

1

Ingest

Upload PDFs, docs, web pages. Chunk into optimal segments with overlap for context.

2

Embed

Convert chunks to vector embeddings using OpenAI or Cohere embedding models.

3

Retrieve

User query is embedded and matched against stored vectors using semantic similarity search.

4

Generate

Retrieved context + user query sent to LLM. Grounded, accurate answer with citations.

Architecture Deep Dive

AI Agent — How Autonomous Agents Work

Agents are the next frontier of AI. You'll build agents that think, plan, use tools, and complete tasks autonomously.

The Agent Loop

1

Receive Task

User gives a complex goal like 'Research competitors and create a report'

2

Plan

Agent breaks the goal into subtasks and decides which tools to use

3

Execute

Agent calls tools — web search, code execution, file I/O, APIs

4

Observe

Agent evaluates tool results and decides if more steps are needed

5

Deliver

Agent compiles results and returns the completed output

Agent Tools You'll Implement

Web Search

Search the internet for real-time information

Code Execution

Run Python/JS code in sandboxed environments

File Operations

Read, write, and process files and documents

API Calls

Interact with external services and databases

Memory

Remember context across conversation turns

Reasoning

Chain-of-thought and step-by-step planning

Your AI Learning Path

A structured journey from fundamentals to shipping production AI applications.

Foundation

Weeks 1-3

LLM Fundamentals

How language models work, tokens, context windows, temperature, and model selection

API Integration

Set up Anthropic, OpenAI, and Gemini SDKs. Make your first API calls with streaming

Prompt Engineering

System prompts, few-shot examples, chain-of-thought, and output formatting

Core Building

Weeks 4-7

AI Chatbot

Multi-turn chatbot with conversation memory, context management, and streaming UI

RAG Pipeline

Document ingestion, chunking, embedding, vector storage, retrieval, and generation

Function Calling

Tool use with Claude and GPT. Define tools, handle function calls, return results

Advanced

Weeks 8-10

AI Agents

Autonomous agents with planning, tool use, web search, code execution, and memory

Production Patterns

Error handling, rate limiting, caching, cost optimization, and monitoring

Portfolio Project

Build and deploy a complete AI application that showcases your skills

AI Tech Stack You'll Master

The complete toolbox for building production AI applications.

LLM Providers

Anthropic Claude OpenAI GPT Google Gemini Mistral Meta Llama Cohere

Frameworks & SDKs

LangChain LlamaIndex Anthropic SDK OpenAI SDK Vercel AI SDK Instructor

Vector Databases

Pinecone Weaviate Chroma pgvector Qdrant Milvus

Deployment & Infra

Streaming APIs WebSockets Edge Functions Docker Railway Vercel AWS Lambda

Real Projects You'll Ship

Not toy demos. Production-grade AI applications you'll deploy and add to your portfolio.

Customer Support Chatbot

AI chatbot trained on company docs that handles customer queries with context-aware responses, escalation logic, and human handoff.

Stack: Claude API, RAG, Streaming, React

Document Q&A System

Upload any PDF or doc and ask questions. RAG pipeline with chunking, embeddings, vector search, and cited answers.

Stack: OpenAI, Pinecone, LangChain, Next.js

Research Agent

Autonomous agent that searches the web, reads articles, extracts key insights, and generates structured research reports.

Stack: Claude, Tool Use, Web Search, Python

Code Review Assistant

AI tool that reviews pull requests, identifies bugs, suggests improvements, and explains changes to team members.

Stack: Claude API, GitHub API, Webhooks

Who Is This For?

Students who want to build AI products, not just use ChatGPT
Developers looking to add AI/ML skills to their stack
Anyone who wants to understand how AI applications actually work under the hood
Engineers preparing for AI-first companies and roles in the age of AI

Frequently Asked Questions

What AI applications will I learn to build?
You'll build AI chatbots with multi-turn context, RAG pipelines for document Q&A, autonomous AI agents with tool use, and production LLM integrations using Claude, GPT, and Gemini APIs.
Do I need machine learning experience?
No ML experience needed. This track focuses on building AI applications using LLM APIs and frameworks, not training models from scratch. Basic programming knowledge is sufficient.
Which AI platforms and tools will I use?
You'll work with Anthropic Claude, OpenAI GPT, Google Gemini, LangChain, LlamaIndex, Pinecone, Weaviate, Vercel AI SDK, and more. You'll learn to choose the right tool for each use case.
Will I build real projects or just follow tutorials?
100% real projects. You'll build and deploy working AI applications — chatbots, RAG systems, AI agents — that you can show to employers and add to your portfolio.
What's the difference between AI Building and AI-Powered Dev?
AI Building teaches you to create AI applications (chatbots, RAG, agents). AI-Powered Dev teaches you to use AI tools (Claude Code, Cursor, Windsurf) to write code faster. Many students do both.
Can I use these skills to build my own AI startup?
Absolutely. The skills you learn — LLM integration, RAG, agents, prompt engineering — are exactly what AI startups are built on. Many of our alumni go on to build their own AI products.

Start Building AI Today

Join the AI & ML Building track and ship your first AI application within weeks. Your senior engineer mentor will guide you every step of the way.

Apply Now — Takes 2 Minutes