Build AI Apps Fast-OpenAI, Streamlit for Product Innovators

From Prompt to Product: Create Functional AI Tools with Open AI Agents & Streamlit Without Being a Developer

Created bySrirama Athreya Mukunda

Last updated 7/2025

English

What you'll learn

Understand AI Foundations: Learn how tokens work and choose the right OpenAI model to bring your ideas to life.
Use the OpenAI Platform & Playground: Build and test AI agents easily—no coding needed, just prompts and smart configs.
Create Real AI-Powered Apps just Using Prompts: Build MVPs like a user story generator using AI agents and Streamlit—step by step.
Integrate AI agents with Real Data: Connect your agents to tools like JIRA to make your apps useful and business-ready.
Think Like an AI to Debug: Fix issues faster by learning how AI agents think and respond to your prompts.
Using Vector Stores & LangChain: Give memory to your agents—store, retrieve, and use context in smarter ways.
Build MVPs, Not Just Prompts: Go from raw idea to fully working product—all with AI, agents, and fast prototyping.
Adopt a Product Builder Mindset: Design useful tools with AI—not just experiments, but real-world applications.
Create Reusable Prompt Templates: Design prompt patterns that work across use cases to build any AI app quickly
Write System Instructions That Guide: Craft system instructions that shape how your OpenAI assistant thinks and responds.
Integrate with External Data Sources: Learn how to connect AI agents with tools like JIRA to fetch, process, and act on real-world data.

Course content

6 sections • 23 lectures • 2h 55m total length

How is this couse different?3:02
Understand:
- Value proposition of the course
- What to expect in the course
Introduction to the Course4:37
- Get a roadmap of what’s covered
- Learn about the course structure and how to get the most out of it
Prerequisites for the course5:20
- Identify required tools, accounts, cost and basic knowledge
- GitHub copilot vs ChatGPT

What are tokens4:09
- Understand token and why it matters for LLMs
- Cost implications of Tokens on various models
Introduction to Open AI Models6:29
- Get a big-picture overview of different OpenAI models
- Learn about their capabilities and ideal use cases
Framework to determine which models to use5:52
- Discover a step-by-step method to pick the right model
- Learn to evaluate models based on your use case needs

Introducing Open AI platform3:26
- Navigate the OpenAI platform interface
- Understand key features and dashboards
Create your first assistant on Open AI playground7:00
- Follow a hands-on guide to building a simple assistant
- Learn the basics of assistant setup
- Learn how to track usage and set user limits
Deepdive into assistants on Open AI playground12:58
- Explore advanced configuration options for assistants such as models, temperature
- Experiment with system instructions and compare results

Getting started with VSCODE and Github Copilot4:36
- Set up VSCode as IDE
- Learn how GitHub Copilot can accelerate your workflow
Installing Python2:17
- Walk through the installation on Windows and Mac
- Set up your system PATH and verify your installation
Getting started with basics, creating virtual env , hello world17:34
- Learn how to create and activate a virtual environment
- Write and run your first “Hello World” Python script

Integrating your assistant with third-party data sources (JIRA )9:07
- Understand constraints of the user story generator app
- Introduce enhanced framework to make more robust applications by integrating third party data
- Introducing case study - JIRA project board
Building utilities script for JIRA project8:36
- Initial Prompt to generate utilities script
- Comparison of same prompt on ChatGPT vs GitHub Copilot
Building along JIRA integration with OpenAI - Part 114:00
- Start developing a real integration step by step
- Start with initial prompt and troubleshoot basic issues
Building along JIRA integration with OpenAI - Part 23:50
- Continue building with prompts
- Defining functional issues while building complex applications
Hacking into thinking of AI tools and using deepsearch to troubleshoot11:46
- Tips and tricks :Learn how to “think like an AI” for problem-solving
- Define a standard prompt template that can be used to build any use case
- Using deep search to identify the libraries which can resolve functional issues
Demo of the Langchain version of the main app13:32
- See a live demo of the app which uses Langchain version of JIRA + Langchain + OpenAI chat completion apis
- Understand benefits and constraints of this model
Vector stores on OpenAI platform7:10
Prompts and demo of final version of JIRA integration with OpenAI11:26
- Enhancing prompt to build final version which uses vector store, ability to download JIRA data & using dynamic system instructions for the streamlit app
- Writing system instructions with best practices to serve the open ai assistant
- Watch a demo of the finished integration in real scenarios - Open AI Assistant + Vector store + JIRA data + Dynamic system instructions stored in a local MD file
Summary ,Takeaways and final thoughts4:12
- Recap key lessons and next steps
- What can you build next?

Requirements

A laptop or desktop with an internet connection
The mindset of a builder -> even if you've never built before
AND NO. You don’t need advanced programming skills
Chat GPT or Github Copilot subscription

Description

Bring your ideas to life with AI—fast.
This hands-on course is designed for product thinkers, innovators, and non-coders who want to go beyond just experimenting with AI and actually build something real. Using OpenAI, Streamlit, and “vibe coding” techniques, you’ll learn how to rapidly prototype intelligent apps—without writing traditional code.

You’ll start by mastering prompt engineering and understanding how different OpenAI models work. Then, you’ll design responsive AI assistants in the OpenAI Playground, integrate tools like JIRA, and build complete MVPs using Streamlit—all by iterating step-by-step with AI tools.

Whether you’re a product manager looking to build proof-of-concepts or a creative entrepreneur prototyping your next big idea, this course will give you the mindset, tools, and techniques to build fast with AI.

Learn how to build AI agents using prompt engineering and system instructions—no coding required.
Master the OpenAI Playground, ChatGPT, and Streamlit to prototype and test real-world AI apps.
Build full working MVPs, like a user story generator or JIRA-integrated assistant, using AI and visual tools.
Connect your assistant to real-world data sources (e.g. JIRA) to create useful, production-ready workflows.
Develop the “AI builder’s mindset” to think in prompts, iterate quickly, and go from concept to creation in hours.

Who this course is for:

Beginner Python developers curious about building AI app
Product managers looking to prototype with OpenAI without coding deep
Startup founders who want to build AI-powered MVPs quickly
Business analysts interested in automating workflows with LLMs
Innovation leads exploring practical AI integration in enterprise tools
Scrum masters seeking to enhance agile tools using assistants
No-code builders wanting to bridge prompts with real applications
UX designers curious about integrating conversational AI in UI flows
Technical product owners aiming to connect OpenAI with project data
Digital consultants looking to add AI capabilities to client solutions
Early-career data analysts experimenting with prompt-based automation
Agile coaches or facilitators exploring AI co-pilots for team rituals
Corporate trainers creating AI-enabled internal tools for training
Learning designers building prompt-based knowledge assistants
Knowledge workers who want to automate repetitive documentation tasks
Technical support professionals wanting to build internal AI bots
JIRA admins looking to enhance reporting and ticket workflows with AI
Operations managers exploring LLM-powered process improvements
Non-developers who want to build working AI tools with little code
AI enthusiasts who want to go beyond ChatGPT and build usable apps
Lead product managers trying to ideate and validate AI use cases rapidly
Senior product leaders exploring how to turn AI concepts into working prototypes
Lead PMs experimenting with OpenAI to identify product opportunities
Product strategists evaluating LLM-powered workflows and automation ideas

Build AI Apps Fast-OpenAI, Streamlit for Product Innovators

What you'll learn

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Course content

Introduction to the course3 lectures • 13min

The World of LLMs3 lectures • 17min

Getting to know Open AI Platform3 lectures • 23min

Setting up your dev environment3 lectures • 24min

Building simple apps using prompts - A basic user story generator2 lectures • 15min

Building complex apps using prompts - Integrating Open AI agent with JIRA9 lectures • 1hr 24min

Requirements

Description

Who this course is for: