11.02.2025
LLM Engineering in Practice with Streamlit and OpenAI
with
Petar Petrov
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 hours of content
241 students
$99.00
Lifetime access
14-Day Money-Back Guarantee
What you get:
- 4 hours of content
- 7 Downloadable resources
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
LLM Engineering in Practice with Streamlit and OpenAI
A course by
Petar Petrov
$99.00
Lifetime access
14-Day Money-Back Guarantee
What you get:
- 4 hours of content
- 7 Downloadable resources
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
$99.00
Lifetime access
$99.00
Lifetime access
14-Day Money-Back Guarantee
What you get:
- 4 hours of content
- 7 Downloadable resources
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
What You Learn
- Build interactive and user-friendly web applications using Streamlit and Python.
- Master prompt engineering to design, refine, and test effective prompts for AI applications.
- Gain a solid understanding of the development process for AI-powered applications.
- Learn how to leverage large language models (LLMs) for real-world use cases.
- Understand how to create activity diagrams to plan and map out your application's architecture.
- Learn how to tackle real-world challenges, including prompt injections, hallucinations, and scaling applications.
Top Choice of Leading Companies Worldwide
Industry leaders and professionals globally rely on this top-rated course to enhance their skills.
Course Description
Learn for Free
![Hosting an LLM vs Using an API](https://cf-images.eu-west-1.prod.boltdns.net/v1/jit/6258000438001/65ec3065-7a5e-47e6-ad31-c46456f77f5b/main/1280x720/2m8s/match/image.jpg)
2.1 Hosting an LLM vs Using an API
4 min
![Open-Source vs Closed-Source Models](https://cf-images.eu-west-1.prod.boltdns.net/v1/jit/6258000438001/3b6a8318-fe18-41e4-8276-e48f59356bab/main/1280x720/3m17s660ms/match/image.jpg)
2.2 Open-Source vs Closed-Source Models
7 min
Interactive Exercises
Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.
Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.
![fill-blank-selection exercise](https://365datascience.com/resources/assets/images/fill-blank-selection.webp)
![order exercise](https://365datascience.com/resources/assets/images/order.webp)
![categorize exercise](https://365datascience.com/resources/assets/images/categorize.webp)
![true-false exercise](https://365datascience.com/resources/assets/images/true-false.webp)
![single-choice exercise](https://365datascience.com/resources/assets/images/single-choice.webp)
![multiple-choice exercise](https://365datascience.com/resources/assets/images/multiple-choice.webp)
![flashcards exercise](https://365datascience.com/resources/assets/images/flashcards.webp)
![fill-blank-typing exercise](https://365datascience.com/resources/assets/images/fill-blank-typing.webp)
Curriculum
- 2. Planning stage10 Lessons 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 API4 minOpen-Source vs Closed-Source Models7 minTokens5 minPricing: Hosting an LLM vs Pay-by-Token4 minInitial Prompt Development: Part 15 minInitial Prompt Development: Part 25 minDatabase Design and Schema Development3 minWhat Is an Activity Diagram4 minCreating an Activity Diagram5 minConcluding the Planning Stage2 min - 3. Crafting and Testing AI Prompts5 Lessons 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 Read now2 minThe OpenAI Playground7 minOptimizing Temperature and Top P for Different Use Cases5 minPrompt Engineering for Software Development6 minHow to Test Out a Prompt Template4 min - 4. Getting to Know Streamlit6 Lessons 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 environment6 minStreamlit's Pros and Cons3 minStreamlit Elements: Titles, Headers, and Formatting3 minStreamlit Elements: Text Methods3 minStreamlit Elements: Chat Elements4 minSession State6 min - 5. Developing the prototype9 Lessons 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 Client4 minImplementing the Chat Functionality6 minBuilding the Setup Page7 minEnhancing Chatbot Interaction with Session State6 minRefining Our Project3 minImplementing Feedback Functionality: Part 14 minImplementing Feedback Functionality: Part 27 minUploading Your Project in GitHub5 minDeploying Your Streamlit App4 min
Topics
PythonStreamlitTheoryPrompt EngineeringAI EngineeringProgrammingOpenAI
Course Requirements
- Intermediate programming knowledge in Python is required to get the most out of this course.
- A foundational understanding of AI will enhance your learning experience.
Who Should Take This Course?
Level of difficulty: Advanced
- Perfect for beginners in AI with good Python fundamentals.
- Aspiring developers, app designers, and stakeholders aiming to create impactful solutions using AI.
- Ideal for anyone eager to understand and harness AI's potential.
Exams and Certification
A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.
![Exams and certification](https://365datascience.com/resources/assets/images/exams-and-certification-ds.webp)
Meet Your Instructor
![Petar Petrov](https://365datascience.com/resources/team/au5wx8l0atn-petar.webp)
Petar holds a Bachelor's degree in Computing from Glasgow Caledonian University. With a keen interest in AI and data science, he has worked on various projects, including AI-powered tools, mobile applications, and data science solutions. Petar is dedicated to developing innovative business solutions and applications, helping to simplify complex concepts and drive tech advancement.
What Our Learners Say
365 Data Science Is Featured at
Our top-rated courses are trusted by business worldwide.