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Build AI Applications with OpenAI APIs and ChatGPT Models
Published 12/2025
Duration: 10h 4m | .MP4 1280x720 30fps(r) | AAC, 44100Hz, 2ch | 2.94 GB
Genre: eLearning | Language: English
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 appsusing OpenAI APIs and ChatGPT models- without guessing, copy-pasting random prompts, or fighting vague examples.
This is ahands-on, demo-first course for developerswho want to go beyond the ChatGPT website and actuallyship real AI applicationsusing Python and JavaScript.
You'll follow along as we buildsmall, focused projectsthat 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 adownloadable ZIP(no GitHub required) so you can run the code locally and adapt it to your own stack.
What you'll do in this course
By the end, you'll be able to:
CallOpenAI ChatGPT modelsfrom your own backend using Python (FastAPI) and Node/Express
Designeffective promptsfor explanations, summaries, code generation, and validation
BuildRAG pipelineswith local documents, embeddings, and FAISS for smarter question-answering
Useoutput schemas and parsersto get reliable JSON and structured data back from the model
Set upprompt pipelines and automated testsso you can safely improve prompts over time
Prepare data and run asmall fine-tuneto align a model with your product or domain
Buildweb & mobile chat UIswith streaming, markdown/code rendering, and conversation state
Orchestrateagents and tools(like a code-exec tool with sandboxed tests and safety checks)
Addtesting, monitoring, logging, and evaluationto your LLM endpoints
Controlcosts, scaling, and rate limitsusing batching, autoscaling simulations, and throttling
Implementsecurity and privacy guardrails: prompt injection defenses, sanitization, and redaction
Exploreadvanced topicslike multimodal (image + text), FAISS sharding, and on-device inference
How the course is structured
The course is organized intoshort, focused modules:
Quick Foundations- A practical mental model for language models, tokens, temperature, and safety
Setup & API Keys- Environment, .env files, secrets best practices, and first API calls
Prompt Engineering- Iterative prompt improvement, roles (system/user/assistant), schemas, pipelines
RAG (Retrieval-Augmented Generation)- Plain prompts vs RAG, local FAISS indexes, context management
Fine-Tuning & Alternatives- Dataset prep, a tiny fine-tune end-to-end, plus retrieval-first patterns
Building Chat Apps- Server/architecture, streaming APIs, React web chat, minimal mobile integration, state
Agents & Tools- Tool calling basics, code execution tool, validation, and guardrails around actions
Testing & Observability- Unit & integration tests for LLM outputs, evaluation harness, simple dashboards
Costs & Ops- Batching vs naive calls, autoscaling + backpressure simulation, rate limiting & throttling
Security & Responsible AI- Prompt injection demos, sanitization/validation pipeline, retention & redaction
Advanced Topics & Capstone- Multimodal basics, FAISS sharding, edge/on-device inference, and a final project
Most lectures arelive 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 & prerequisites
We'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 files
APIs:OpenAI Chat / Responses and Embeddings APIs (using your own OpenAI account)
Youdon'tneed 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 for
Backend or full-stack developerswho want to integrate OpenAI APIs into real applications
Front-end engineerswho want to wire a chat UI or features into a backend LLM service
Technical product folks / indie hackerswho can read basic code and want to prototype AI features
Anyone who's used ChatGPT in the browser and now wants tobuild serious AI-powered featuresin their own apps
If you're ready to stop treating ChatGPT as a toy in the browser and start treating it as apowerful APIyou 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.
More Info
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