LLM Engineering in Practice with Streamlit and OpenAI

Build, optimize, and deploy LLM-based solutions with a hands-on real-world project.

What you'll 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.

Requirements

• Intermediate programming knowledge in Python is required to get the most out of this course.
• Foundational understanding of AI will enhance your learning experience.

Description

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.
• System 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 the booming field of AI development or enhance your existing skill set, this course will empower you to succeed in one of the most exciting and in-demand career paths of the future.

Take the next step in your journey to becoming an AI engineer—enroll today!

Who this course is for:

• 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.