VOOZH about

URL: https://www.coursera.org/learn/building-production-ready-apps-with-large-language-models

⇱ Building Production-Ready Apps with Large Language Models | Coursera

Building Production-Ready Apps with Large Language Models

Keep adding new skills with 10,000+ programs for $239 (usually $399). Save now.

Building Production-Ready Apps with Large Language Models

2,418 already enrolled

Included with

Ask Coursera

Gain insight into a topic and learn the fundamentals.
4.0

29 reviews

Intermediate level

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
4.0

29 reviews

Intermediate level

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Explore AI capabilities with interactive tasks.

  • Build a chatbot using Python and HuggingFace.

  • Deploy a reliable AI app.

  • Evaluate AI ethics through practical scenarios.

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

1 assignment

Taught in English

Build your subject-matter expertise

This course is part of the Harnessing LLMs: Strategy, Fine-Tuning & Evaluation Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There is 1 module in this course

In the age of artificial intelligence (AI), it is essential to learn how to apply the power of large language models (LLMs) for building various production-ready applications. In this hands-on-course, learners will gain the necessary skills for building and responsibly deploying a conversational AI application.

Following the demo provided in this course, learners will learn how to develop a FAQ chatbot using HuggingFace, Python, and Gradio. Core concepts from applying prompt engineering to extract the most value from LLMs to infrastructure, monitoring, and security considerations for real-world deployment will be covered. Important ethical considerations such as mitigating bias, ensuring transparency, and maintaining user trust will also be covered to help learners understand the best practices in developing a responsible and ethical AI system. By the end, learners will have developed familiarity with both the technical and human aspects of building impactful LLM applications. The learners can design, develop, and deploy production-ready applications powered by Large Language Models. This course is designed for individuals with a basic understanding of programming and application development concepts. It is suitable for developers, data scientists, AI enthusiasts, and anyone interested in using LLMs to build practical applications. you need basic concepts, software tools, and an internet-connected computer.

In this course, learners will learn how to develop a FAQ chatbot using HuggingFace, Python, and Gradio. Core concepts from applying prompt engineering to extract the most value from LLMs, to infrastructure, monitoring and security considerations for real-world deployment will be covered.

What's included

12 videos5 readings1 assignment1 ungraded lab

12 videos•Total 57 minutes
  • Introduction to LLMs: Benefits and Applications•4 minutes
  • Prompt Engineering•7 minutes
  • LLM Development•5 minutes
  • Production Readiness•3 minutes
  • Getting Started with HuggingFace•5 minutes
  • Building UIs with Gradio•8 minutes
  • Developing the FAQ Chatbot: Part 1 - Getting Started•7 minutes
  • Developing the FAQ Chatbot: Part 2 - Finalizing and Deployment•6 minutes
  • Ethical Considerations for LLMs•3 minutes
  • Mitigating AI Risks•3 minutes
  • Ensuring Transparency•3 minutes
  • Maintaining User Trust•3 minutes
5 readings•Total 22 minutes
  • Welcome to the Course: Course Overview•5 minutes
  • [Optional] The GPT Generative AI Lab Playground•2 minutes
  • Introduction to Large Language Models•5 minutes
  • A Comprehensive Comparative Analysis of LLMs•5 minutes
  • Best Practices for Deploying Large Language Models (LLMs) in Production•5 minutes
1 assignment•Total 40 minutes
  • Final Assessment•40 minutes
1 ungraded lab•Total 60 minutes
  • [Optional] Access Your GPT GenAI Playground•60 minutes

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructors

Instructor ratings
4.0 (8 ratings)
Coursera
6 Courses•5,183 learners

Explore more from Machine Learning

Why people choose Coursera for their career

👁 Image

Felipe M.

Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
👁 Image

Jennifer J.

Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
👁 Image

Larry W.

Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
👁 Image

Chaitanya A.

"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Learner reviews

  • 5 stars

    68.96%

  • 4 stars

    3.44%

  • 3 stars

    3.44%

  • 2 stars

    10.34%

  • 1 star

    13.79%

Showing 3 of 29

XM
·

Reviewed on Apr 30, 2024

0 points are given for a correct answer to the 1st question.

Frequently asked questions

In this course, it means turning large language model behavior into an application that is usable, reliable, and responsibly deployed. The focus is not only on getting model outputs, but on building an app with prompting, interface design, monitoring, security, and ethical safeguards.

You would use it when you want an LLM to support an actual application instead of just answering prompts in isolation. The course emphasizes situations where consistent behavior, clear boundaries, and operational practices matter as much as the generated response itself.

It sits in the build-and-test phase where model behavior, app logic, and user interaction are connected into one repeatable system. In this course, that means moving from isolated prompting toward an application workflow that can be deployed, monitored, and improved over time.

A production-ready LLM app is built for dependable use, while a basic AI demo mainly shows that the model can generate an answer. Here, the difference is that production work adds reliability, security, monitoring, and user-trust considerations instead of stopping at a working prototype.

A basic understanding of programming and application development concepts is helpful before starting. Because the course is intermediate, it also helps to be comfortable working with code and software tools while building and refining an application.

The course mainly uses Python, HuggingFace, and Gradio to build and present an LLM application. It also emphasizes prompt engineering and production practices such as monitoring, security, and reliability.

You practice shaping prompts, building a conversational interface, organizing application logic, and preparing an LLM app for deployment. You also apply monitoring, security, and ethical checks so the workflow supports more reliable, transparent, and trustworthy use.

Financial aid available,