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LLM Engineering in Practice with Streamlit and OpenAI

Name: LLM Engineering in Practice with Streamlit and OpenAI Course
Price: 36 USD

Dive into the world of AI solutions with this hands-on course. Learn how to design, develop, and deploy an interview simulator project using Streamlit, integrating language models, feedback systems, and advanced prompts. Master key concepts in AI, prompt engineering, and app development while solving real-world challenges.

4.8

863 reviews on

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1,732 students already enrolled

Skill level:

Advanced

Duration:

4 hours

Lessons (3 hours)

Practice exams (30 minutes)

CPE credits:

3.5

CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited

certificate

What you learn

Build interactive web apps using Streamlit and Python.
Master prompt engineering for effective AI application design.
Understand how to develop AI-powered apps using LLM APIs.
Create activity diagrams to plan and structure your app’s architecture.
Tackle challenges like prompt injections, hallucinations, and scaling.

Topics & tools

PythonStreamlitTheoryPrompt EngineeringAI EngineeringProgrammingOpenAIAIGit

Your instructor

Petar Petrov

Software Developer at 365 Data Science | Programmer | AI Engineer

Track record

Bringing real-world expertise from leading global companies

Academic background

Bachelor's degree, Computing and Business

Bachelor’s Degree in Computing, Glasgow Caledonian University

Ask Petar a question

Course overview

Accredited certificates

Course OVERVIEW

Description

CPE Credits: 3.5 Field of Study: Information Technology
Delivery Method: QAS Self Study

Are you ready to dive into the fascinating world of AI-powered applications?

Do you want to solve real-world problems using cutting-edge large language models (LLMs)?

This is the perfect course for you!

This course is your step-by-step guide to designing, developing, and deploying an AI application using Streamlit, Python, and OpenAI models. You’ll not only learn the theory and development process but also gain hands-on experience with a practical, real-world example: ACE Interview, a powerful AI-driven interview application that has already helped thousands of people prepare for their interviews. By exploring the structure of ACE Interview, you’ll see how the concepts taught in this course are applied in practice. Moreover, we’ll share the challenges and mistakes we encountered during its development—and how we overcame them—so you can avoid similar pitfalls in your own projects.

By completing this course, you’ll acquire a versatile and highly practical skill set including:

Python Programing with Streamlit – Learn to build interactive, user-friendly web apps using one of the most popular frameworks.
Prompt Engineering – Master the art of designing, refining, and testing prompts to maximize the performance of your AI projects.
Architecture Design – Learn to create activity diagrams to visually map out your application’s structure, making it easier to plan and communicate your ideas effectively.
Utilizing LLMs: Understand how to leverage large language models for different use cases, including the differences between hosting models and using APIs, as well as open-source versus closed-source options.
Cost Management: Analyze and predict the cost associated with your AI projects to make informed decisions.

Our course takes you through every stage of the development process:

Planning stage: Design the architecture, database and prompts to lay a strong foundation for your project.
Prototype stage: Build a fully functional Streamlit to showcase in your portfolio.
Development stage: Explore the real-world challenges you may encounter while working on your project and learn effective strategies to solve them. These include issues like prompt injections, handling hallucinations, scaling your application, optimizing token usage, and managing cost to ensure your project is both efficient and scalable.

By the end of this course, you’ll have more than just a working prototype of an AI interview simulator—you’ll have the knowledge and confidence to create your own AI-powered applications. Whether you’re looking to break into AI development or enhance your existing skill set, this course is designed to help you succeed.

Take the next step in your AI journey—enroll today!

Prerequisites

Python (version 3.8 or later), Streamlit library, OpenAI API key, and a code editor or IDE (e.g., VS Code or Jupyter Notebook)
Intermediate Python skills are required. Familiarity with APIs.
Familiarity with basic statistics and linear algebra is helpful but not mandatory.

Advanced preparation

Curriculum

45 lessons 2 exams

1. Introduction to the Course

11 min

This section sets the stage for your journey into creating cutting-edge AI-driven projects. Learn about the ACE Interview tool as a real-world example of successful AI integration and understand the skills you’ll gain by completing this course.

11 min

This section sets the stage for your journey into creating cutting-edge AI-driven projects. Learn about the ACE Interview tool as a real-world example of successful AI integration and understand the skills you’ll gain by completing this course.

Introduction to the Course Free

Your Resources Hub! Free

What does the course cover? Free

The Interview Tool’s Specifics Free
2. Planning stage

44 min

Lay the groundwork for your AI-powered application by mastering the planning process. In this section, you’ll explore key decisions like hosting LLMs vs. using APIs, open-source vs. closed-source models, and cost analysis. Additionally, you’ll learn how to design activity diagrams, database schemas, and prompts to ensure a strong foundation for your project.

44 min

Lay the groundwork for your AI-powered application by mastering the planning process. In this section, you’ll explore key decisions like hosting LLMs vs. using APIs, open-source vs. closed-source models, and cost analysis. Additionally, you’ll learn how to design activity diagrams, database schemas, and prompts to ensure a strong foundation for your project.

Hosting an LLM vs Using an API Free

Open-Source vs Closed-Source Models Free

Tokens

Pricing: Hosting an LLM vs Pay-by-Token

Initial Prompt Development: Part 1

Initial Prompt Development: Part 2

Database Design and Schema Development

What Is an Activity Diagram

Creating an Activity Diagram

Concluding the Planning Stage
3. Crafting and Testing AI Prompts

24 min

You’ll explore the OpenAI Playground to experiment with LLM capabilities, learn how to optimize parameters like temperature and top-p for different use cases, and master prompt engineering for software development. Finally, you’ll test and validate your prompt templates to ensure reliable and accurate AI performance.

24 min

You’ll explore the OpenAI Playground to experiment with LLM capabilities, learn how to optimize parameters like temperature and top-p for different use cases, and master prompt engineering for software development. Finally, you’ll test and validate your prompt templates to ensure reliable and accurate AI performance.

Adding Funds to Your OpenAI API Account

The OpenAI Playground

Optimizing Temperature and Top P for Different Use Cases

Prompt Engineering for Software Development

How to Test Out a Prompt Template

Practice exam
4. Getting to Know Streamlit

25 min

This section introduces you to Streamlit, a powerful framework for building interactive web applications with Python. You’ll learn how to set up your environment, explore Streamlit’s pros and cons, and work with key elements like titles, text methods, chat components, and session state. By the end, you’ll have a strong foundation in Streamlit to develop user-friendly applications.

25 min

This section introduces you to Streamlit, a powerful framework for building interactive web applications with Python. You’ll learn how to set up your environment, explore Streamlit’s pros and cons, and work with key elements like titles, text methods, chat components, and session state. By the end, you’ll have a strong foundation in Streamlit to develop user-friendly applications.

Setting up environment

Streamlit's Pros and Cons

Streamlit Elements: Titles, Headers, and Formatting

Streamlit Elements: Text Methods

Streamlit Elements: Chat Elements

Session State
5. Developing the prototype

46 min

In this section, you’ll bring an AI application to life by building a fully functional prototype of an interview app. Learn how to initialize an OpenAI client, implement chatbot functionality, and enhance interactions using session state. You’ll also dive into adding feedback features, refining your project, and deploying your application to GitHub and Streamlit, making it ready for real-world use.

46 min

In this section, you’ll bring an AI application to life by building a fully functional prototype of an interview app. Learn how to initialize an OpenAI client, implement chatbot functionality, and enhance interactions using session state. You’ll also dive into adding feedback features, refining your project, and deploying your application to GitHub and Streamlit, making it ready for real-world use.

Initializing an OpenAI Client

Implementing the Chat Functionality

Building the Setup Page

Enhancing Chatbot Interaction with Session State

Refining Our Project

Implementing Feedback Functionality: Part 1

Implementing Feedback Functionality: Part 2

Uploading Your Project in GitHub

Deploying Your Streamlit App
6. Solving Real-World AI Challenges

49 min

49 min

Introduction

Application Structure

Prompt Structure of HR Interviews

Prompt Structure of Technical Interview

Additional Protection from Errors

Hallucinations

Prompt Injection

Counting Tokens

Cost Reduction

Scaling

Conclusion
7. Course exam

40 min

40 min

Course exam

Free lessons

1.1 Introduction to the Course

3 min

1.3 What does the course cover?

2 min

1.4 The Interview Tool’s Specifics

5 min

2.1 Hosting an LLM vs Using an API

4 min

2.2 Open-Source vs Closed-Source Models

7 min

Start for free

Early in my career, I took 365 Data Science courses that built a strong foundation in data science and financial analysis with Excel. The detailed, visual content—from basics to advanced—helped me grasp key concepts, develop practical skills, and apply them to real projects in multiple industries. The instructors, quick responses to questions, and collaborative community made learning even more engaging. 365 truly supports continuous growth, and I recommend it to anyone starting or advancing in data and AI.

Saquib Rahman K.

After 365: Data Scientist | EXL

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96%

of our students recommend

365 Data Science.

So far, I am in love with this course. Being taught in a very clear, organized manner. And the medal is to the recap questions because they really come through in solidifying one's understanding.

Isaac K.

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$29,000

average salary increase

after moving to an AI and data science career

The courses at 365 Data Science are engaging, well-structured, and strike the right balance between theory and application. The end-of-course exams stand out—they’re challenging enough to ensure you’ve truly mastered the material, making the certification well-earned. For me, it was also a great refresher that helped solidify my past knowledge. If you want a comprehensive, hands-on platform that truly tests and strengthens your skills, I highly recommend 365 Data Science.