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Build AI Apps with Python & LLM APIs

Go from API key to deployed chatbot, document Q&A and AI tools you can show recruiters

Ghosla Academy · AI Team

This course teaches you to build working applications on top of large language model APIs using plain Python. You will learn how requests, tokens and model settings work, then build progressively richer apps: a chatbot, a document Q&A system using retrieval, and a data extractor that returns clean JSON. Along the way we cover the engineering habits that matter in real jobs: storing secrets safely, handling rate limits, keeping costs predictable, testing outputs and deploying a demo that recruiters can open in a browser.

What you will be able to do

  • Call LLM APIs from Python and handle tokens, temperature and errors
  • Design system prompts and structured JSON outputs for reliable apps
  • Build retrieval-augmented generation (RAG) with embeddings and a vector store
  • Add tool/function calling so a model can use your own Python functions
  • Build simple web interfaces with Streamlit and deploy them
  • Keep API keys safe, control costs and evaluate answer quality

What you build

  • College FAQ chatbot with a Streamlit interface
  • PDF question-answering app using embeddings
  • Structured data extractor for invoices (JSON output)

What you need to start

  • Basic Python (variables, functions, lists, dicts)
  • A laptop that can run Python 3.10+
  • Willingness to create an API account (free credits are often enough to practise)

Curriculum · 8 sections, 30 lectures

Setup and your first API call4 lectures
  1. How LLM APIs work: requests, responses, tokensFree preview8 min
  2. Installing Python, venv and the SDKFree preview12 min
  3. Your first completion call10 min
  4. Keeping API keys out of your code with .env7 min
Controlling model behaviour4 lectures
  1. System, user and assistant messages12 min
  2. Temperature, max tokens and stop sequences10 min
  3. Getting structured JSON output14 min
  4. Quiz: model parameters6 min
Building a chatbot4 lectures
  1. Keeping conversation history14 min
  2. A Streamlit chat interface12 min
  3. Handling errors, timeouts and rate limits10 min
  4. Project: college FAQ chatbot20 min
Embeddings and retrieval (RAG)5 lectures
  1. What embeddings are and why they help12 min
  2. Splitting PDFs into chunks15 min
  3. Storing and searching vectors in ChromaDB14 min
  4. Writing grounded answers with citations12 min
  5. Project: PDF Q&A app22 min
Tool and function calling3 lectures
  1. Letting the model call your Python functions12 min
  2. Building a currency and GST calculator tool14 min
  3. Safety limits for tool use10 min
Data extraction apps3 lectures
  1. Extracting fields from messy text12 min
  2. Validating output with Pydantic10 min
  3. Project: invoice data extractor20 min
Quality, cost and safety4 lectures
  1. Simple evaluation sets for your app10 min
  2. Estimating and reducing token costs9 min
  3. Prompt injection basics and defences10 min
  4. Quiz: production readiness6 min
Deploying and showcasing3 lectures
  1. Deploying a Streamlit app for free12 min
  2. Writing a strong GitHub README10 min
  3. Talking about AI projects in interviews8 min

Who this is for

  • Engineering and BCA/MCA students wanting AI portfolio projects
  • Python learners who want to move beyond notebooks into real apps
  • Freshers preparing for AI/GenAI developer roles