AI Developer Program: Build Gen AI and AI Agents, RAG Systems & LLM Apps with Python
Go from zero to shipping real AI products in Python — agents with memory and tools, RAG systems, voice assistants, image AI and your own LLM service. 136 lessons, 8 hands-on projects.
check_circle Recordings included check_circle 1:1 doubt solving check_circle Mock interviews
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Taught live
by working pros
1:1 mentorship
never stuck alone
Mock interviews
get job-ready
Placement help
until you land
Overview
About this program.
This program closes that gap. You start with how LLMs really behave and how to prompt them properly — zero-shot, few-shot, chain-of-thought, ReAct and context engineering — then spend the rest of the course building. You'll build AI agents that call your own functions, remember conversations across sessions using SQLite and PostgreSQL, fetch live data like weather and location, and run inside a real web app with sessions and a styled front end.
From there it widens: run models locally with Ollama so you aren't tied to any vendor, build your own LLM API service with chat-history support, force structured JSON out of models so your code can trust the output, and automate real work — Google Sheets pipelines that summarise new rows and email them on a schedule.
Then the heavier projects. A recipe generator that reads photos of ingredients. Image generation with Google's models. A voice assistant that records audio, transcribes it, thinks and speaks back. A document context-injection system and a full RAG pipeline with embeddings, vector stores and text splitting. A web-research agent powered by Tavily that writes articles from live search. And finally, a local AI coding agent — your own Claude Code clone — with the ReAct loop, streaming tool calls, file read/write and directory access, polished console output and all.
The program closes where most courses don't go at all: security. You'll learn what AI coding agents get wrong — access control, business logic, SSRF, rate limiting, CSRF — why prompt engineering alone won't fix insecure AI code, and what privacy and data retention actually mean when your app sends user data to a model provider.
22 sections. 136 lessons. Roughly 13.5 hours of focused, code-along video, every lesson linked back to its source course so you always know where you are.
Is this you?
Built for people who are ready to start.
Complete beginners
No coding background needed — we start from the fundamentals and build up.
Career switchers
Moving into tech from another field and want a structured, mentored path.
Students & fresh grads
Turn your degree into job-ready, portfolio-backed practical skills.
Working professionals
Level up your stack, fill gaps, and prepare for a better role.
Outcomes
What you'll walk away with.
- check_circle Build AI agents from scratch that use function calling to run your own Python tools
- check_circle Give agents real memory — in-session, SQLite, and persistent PostgreSQL on Supabase
- check_circle Write prompts that actually work: zero-shot, few-shot, chain-of-thought, ReAct and context engineering
- check_circle Turn a CLI agent into a full web app with sessions, POST handling, styling and GPS location
- check_circle Run models locally and offline with Ollama, and wire them into Python via LangChain
- check_circle Build and ship your own LLM API service with chat-history support
- check_circle Force reliable structured JSON output from LLMs and agents so your code can trust it
- check_circle Build a complete RAG system: text splitting, embeddings, vector stores and grounded answers
- check_circle Work with images and audio — interpret photos with Gemini and OpenAI, generate images, build a voice assistant
- check_circle Automate real workflows with Google Sheets, scheduled jobs and automated AI email summaries
- check_circle Build a local AI coding agent (a Claude Code clone) with streaming, tool use and file access
- check_circle Spot the security holes AI-generated code leaves behind — RBAC, SSRF, rate limiting, CSRF and data retention
Curriculum
The syllabus.
01 Module 1: Introduction to AI-Powered Apps
- arrow_right_alt Foundations of LLMs and chatbots
- arrow_right_alt What makes an AI app different from a normal app
- arrow_right_alt RAG systems explained
- arrow_right_alt AI agents explained
- arrow_right_alt Build your first AI-powered Python app
02 Module 2: Prompt Engineering Fundamentals
- arrow_right_alt Anatomy of a formal prompt
- arrow_right_alt Zero-shot and few-shot prompting
- arrow_right_alt Chain of thought and ReAct prompting
- arrow_right_alt Context engineering
- arrow_right_alt Engineering a real system prompt
03 Module 3: Building AI Agents
- arrow_right_alt What an AI agent actually is
- arrow_right_alt Function calling for LLMs
- arrow_right_alt Building your first agent
- arrow_right_alt Adding tools, then multiple tools
- arrow_right_alt Writing a good agent system prompt
04 Module 4: Building a Real-World AI Agent
- arrow_right_alt Agent that returns live weather data
- arrow_right_alt Getting the user's location
- arrow_right_alt Improving and shaping the agent's output
05 Module 5: AI Agents with Memory
- arrow_right_alt Enabling agent memory
- arrow_right_alt How memory actually works under the hood
- arrow_right_alt Handling multiple conversations
- arrow_right_alt Continuous conversation loops
06 Module 6: Persistent Database Memory
- arrow_right_alt Saving and retrieving history in SQLite
- arrow_right_alt PostgreSQL architecture for agents
- arrow_right_alt Setting up Supabase
- arrow_right_alt Persisting conversations to the database
07 Module 7: Making an AI Agent Web App
- arrow_right_alt From CLI to web app
- arrow_right_alt Building the agent homepage
- arrow_right_alt POST requests and agent responses
- arrow_right_alt Session objects
- arrow_right_alt CSS styling
- arrow_right_alt Browser GPS location
08 Module 8: Self-Hostable Models
- arrow_right_alt Overview of self-hostable models
- arrow_right_alt Installing and using Ollama
- arrow_right_alt Pulling models from the command line
- arrow_right_alt Using a local model through Python and LangChain
09 Module 9: Building Your Own LLM Service
- arrow_right_alt How an LLM service works
- arrow_right_alt Preparing the environment
- arrow_right_alt Building the API
- arrow_right_alt Adding chat-history support
10 Module 10: Structured Output from LLMs
- arrow_right_alt Why structured data matters
- arrow_right_alt Structured output with LangChain
- arrow_right_alt Structured output from agents
11 Module 11: Project — Spreadsheet & AI Automation
- arrow_right_alt Google credentials and sheet setup
- arrow_right_alt Loading sheet data into Python
- arrow_right_alt Detecting newly added rows
- arrow_right_alt Sending rows to an LLM
- arrow_right_alt Emailing the AI summary
- arrow_right_alt Scheduling automatic runs
12 Module 12: Project — Viewing & Editing Private Google Sheets
- arrow_right_alt Connecting Google Sheets to Python
- arrow_right_alt Reading private sheet data
- arrow_right_alt Adding new rows
- arrow_right_alt Updating existing rows and cells
13 Module 13: Interpreting Images with Python & LLMs
- arrow_right_alt Sending text to Gemini
- arrow_right_alt Interpreting images with Gemini models
- arrow_right_alt Interpreting images with OpenAI models
14 Module 14: Project — Recipe Generator from Images
- arrow_right_alt Generating recipes from ingredients in a photo
- arrow_right_alt Building a Gradio web app
- arrow_right_alt Shipping the recipe generator
15 Module 15: Image Generation with Python and AI
- arrow_right_alt OpenAI pricing and model overview
- arrow_right_alt Google AI Studio walkthrough
- arrow_right_alt Google AI pricing and billing setup
- arrow_right_alt Generating images with Google models
16 Module 16: Building an AI Voice Assistant
- arrow_right_alt The data workflow
- arrow_right_alt Recording audio with Python
- arrow_right_alt Speech to text with OpenAI
- arrow_right_alt Sending text to the LLM
- arrow_right_alt Text to speech back to the user
17 Module 17: Document Context Injection System
- arrow_right_alt System architecture
- arrow_right_alt Initializing the LLM
- arrow_right_alt System prompt and context injection
- arrow_right_alt Loading documents
- arrow_right_alt Building the context
- arrow_right_alt Continuous conversation over documents
18 Module 18: Project — Building a Full RAG System
- arrow_right_alt How RAG works
- arrow_right_alt Setting up the LLM
- arrow_right_alt Text splitting playground
- arrow_right_alt Embeddings and vector stores
- arrow_right_alt Creating embeddings from documents
- arrow_right_alt Getting grounded answers
- arrow_right_alt RAG architecture
19 Module 19: Agents with Web Search
- arrow_right_alt Setting things up
- arrow_right_alt What Tavily is
- arrow_right_alt Coding a web-search script
- arrow_right_alt Performing multiple searches
- arrow_right_alt Generating articles from live results
20 Module 20: Project — Local AI Agent (Claude Code Clone)
- arrow_right_alt The agent ReAct pattern
- arrow_right_alt Creating a simple agent
- arrow_right_alt Adding file access
- arrow_right_alt Streaming agent updates
- arrow_right_alt Streaming tool use and tool results
- arrow_right_alt Displaying the final answer
21 Module 21: Project — Polishing the Local AI Agent
- arrow_right_alt Stylizing console output
- arrow_right_alt Stylizing the main answer
- arrow_right_alt Write-file capability
- arrow_right_alt Read-file and create-directory privileges
- arrow_right_alt Finalizing the agent
22 Module 22: Building Safely with AI
- arrow_right_alt What LLM app sec is
- arrow_right_alt Privacy and data retention in production
- arrow_right_alt The dark side of vibe coding
- arrow_right_alt Where AI agents fail — RBAC, business logic, SSRF, rate limiting, CSRF
- arrow_right_alt Why prompt engineering won't fix insecure AI code
Why live
You've tried learning alone. How'd that go?
Free tutorials
Endless videos, nobody to answer your "why", and no one checking your code. Most people quit.
Recorded courses
You watch alone, on your own willpower, with zero accountability. Completion rates are famously low.
This live cohort
A real mentor answers you in real time, reviews your work, and a cohort keeps you accountable to the finish.
Included with enrollment
Everything you get. One price.
All of the above is included — no add-ons, no upsells, no surprise fees later.
Complete program
$100
Zero-risk enrollment
Enroll with confidence.
Talk before you pay
Message an advisor and get honest answers about the batch and syllabus — no pressure, no hard sell.
Never fall behind
Every class is recorded and yours for life. Miss a session or need a rewatch — it's always there.
Try us free first
Not sure yet? Start with our free interactive courses and see exactly how we teach before you invest.
Your mentor
Learn from someone who does the job.
Naushad Sheikh
Director
Before you ask
Questions, answered.
Do I need prior experience?
No. This program is built to take you from the fundamentals to job-ready. If you can use a computer, you can start — and your mentor and 1:1 sessions make sure you never get stuck alone.
What if I miss a live class?
Every session is recorded and uploaded, and you keep lifetime access. You can also bring anything you missed to your 1:1 doubt-solving sessions with the instructor.
What exactly is included in the fee?
Everything listed above — live classes, lifetime recordings, 1:1 doubt solving, study material, reviewed assignments and projects, interview prep, mock interviews and placement support. One price, no hidden upsells.
Will this actually help me get a job?
That's the goal. Alongside the skills, you get interview preparation, mock interviews with real feedback, resume and LinkedIn review, and placement assistance with referrals shared in our community.
Can I talk to someone before I pay?
Absolutely. Message us on WhatsApp and an advisor will walk you through the batch, the syllabus and whether it fits your goals — no pressure.
Not sure yet — is there a free way to try?
Yes. Our interactive courses and coding challenges are free forever. Start there, see how we teach, and join a live cohort whenever you're ready.
Your next batch starts soon.
Seats in a live cohort are limited by design — small groups mean real attention. Reserve yours before this batch fills.
Not ready to enroll yet?
Start with our free interactive courses and coding challenges — no card, no catch.