Build Ai Applications With Openai Apis And Chatgpt Models
 

Build real-world AI apps with OpenAI APIs and ChatGPT models using Python and JavaScript, from prompts to production.
What you'll learn
Build real-world AI features using OpenAI APIs
Design effective prompts for reliable, controlled outputs
Implement Retrieval-Augmented Generation (RAG) from scratch
Fine-tune models and know when not to
Build end-to-end chat applications
Use agents and tools safely (function calling & code execution)
Test, monitor, and evaluate LLM behavior in production-like setups
Control cost, scale responsibly, and apply safety & privacy best practices
Requirements
Basic programming experience
Familiarity with the command line, terminal, or bash
Visual Studio Code or IDE of your choice installed
OpenAI account or ChatGPT account
Basic HTTP / web familiarity (nice to have, not mandatory)
Basic API knowledge
Interest on how to plug AI into real applications
Description
Build AI-powered features into your apps using OpenAI APIs and ChatGPT models - without guessing, copy-pasting random prompts, or fighting vague examples.This is a hands-on, demo-first course for developers who want to go beyond the ChatGPT website and actually ship real AI applications using Python and JavaScript.You'll follow along as we build small, focused projects that mirror real-world use cases: prompt engineering, retrieval-augmented generation (RAG), fine-tuning, agents and tools, chat UIs, testing, monitoring, cost control, and more. Every demo comes with a downloadable ZIP (no GitHub required) so you can run the code locally and adapt it to your own stack.What you'll do in this courseBy the end, you'll be able to:Call OpenAI ChatGPT models from your own backend using Python (FastAPI) and Node/ExpressDesign effective prompts for explanations, summaries, code generation, and validationBuild RAG pipelines with local documents, embeddings, and FAISS for smarter question-answeringUse output schemas and parsers to get reliable JSON and structured data back from the modelSet up prompt pipelines and automated tests so you can safely improve prompts over timePrepare data and run a small fine-tune to align a model with your product or domainBuild web & mobile chat UIs with streaming, markdown/code rendering, and conversation stateOrchestrate agents and tools (like a code-exec tool with sandboxed tests and safety checks)Add testing, monitoring, logging, and evaluation to your LLM endpointsControl costs, scaling, and rate limits using batching, autoscaling simulations, and throttlingImplement security and privacy guardrails: prompt injection defenses, sanitization, and redactionExplore advanced topics like multimodal (image + text), FAISS sharding, and on-device inferenceHow the course is structuredThe course is organized into short, focused modules:Quick Foundations - A practical mental model for language models, tokens, temperature, and safetySetup & API Keys - Environment, .env files, secrets best practices, and first API callsPrompt Engineering - Iterative prompt improvement, roles (system/user/assistant), schemas, pipelinesRAG (Retrieval-Augmented Generation) - Plain prompts vs RAG, local FAISS indexes, context managementFine-Tuning & Alternatives - Dataset prep, a tiny fine-tune end-to-end, plus retrieval-first patternsBuilding Chat Apps - Server/architecture, streaming APIs, React web chat, minimal mobile integration, stateAgents & Tools - Tool calling basics, code execution tool, validation, and guardrails around actionsTesting & Observability - Unit & integration tests for LLM outputs, evaluation harness, simple dashboardsCosts & Ops - Batching vs naive calls, autoscaling + backpressure simulation, rate limiting & throttlingSecurity & Responsible AI - Prompt injection demos, sanitization/validation pipeline, retention & redactionAdvanced Topics & Capstone - Multimodal basics, FAISS sharding, edge/on-device inference, and a final projectMost lectures are live demos, not slides. You'll see the instructor run the code, inspect responses, explain trade-offs, and then you can replay and follow along using the provided ZIP files.Tech stack & prerequisitesWe'll focus on:Languages: Python and JavaScript/Node (you only need basic familiarity with one of them)Tools: VS Code, pip, npm, simple REST calls (Postman, curl, or your browser), .env filesAPIs: OpenAI Chat / Responses and Embeddings APIs (using your own OpenAI account)You don't need a deep math background or prior ML experience. If you can build a basic web API or script and are comfortable reading code, you're good to go.Who this course is forBackend or full-stack developers who want to integrate OpenAI APIs into real applicationsFront-end engineers who want to wire a chat UI or features into a backend LLM serviceTechnical product folks / indie hackers who can read basic code and want to prototype AI featuresAnyone who's used ChatGPT in the browser and now wants to build serious AI-powered features in their own appsIf you're ready to stop treating ChatGPT as a toy in the browser and start treating it as a powerful API you can build on, this course will walk you through it step by step - from first prompts all the way to tested, monitored, and hardened AI applications.
Who this course is for
backend or full-stack developer who wants to add AI features (chat, summarization, RAG, code helpers) to existing web or mobile apps.
A Python or JavaScript/Node developer who prefers to learn by building real, end-to-end demos rather than just reading API docs.
A technical founder or product engineer exploring how to ship practical AI-powered features quickly and safely.
A data/ML-curious engineer who wants to understand how RAG, fine-tuning, agents, and evaluation fit into real-world systems without diving into heavy math.
People who want to call models from code, design prompts and RAG pipelines, work with agents and tools, and ship production-style features with testing, monitoring, cost control, and safety in mind.