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Build an LLM and RAG-based Chat Application using AlloyDB and LangChain

Google Cloud Training

This is a self-paced lab that takes place in the Google Cloud console. Learn how to create an interactive application within a deployed environment.

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What's inside

Syllabus

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Builds a strong foundation for beginners in using LLM and RAG for interactive applications
Taught by Google Cloud Training, who are recognized for their work in cloud computing
Explores concepts and tools that are highly relevant to industry
Offers hands-on labs and interactive materials that enhance learning
Requires prior knowledge in software development

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Reviews summary

Building llm apps with alloydb & langchain

According to the course description, this lab offers a hands-on experience in building a RAG-based chat application utilizing LLMs. Learners can expect to work within the Google Cloud console, specifically leveraging AlloyDB for data management and LangChain for application development. The focus is on creating an interactive application within a deployed environment. It targets professionals seeking to implement modern AI solutions within the Google Cloud ecosystem.
Leverages Google Cloud ecosystem, including AlloyDB.
"I know this course focuses purely on Google Cloud, which aligns with my professional needs."
"I understand it involves working directly within the Google Cloud console."
"I anticipate using AlloyDB for the database component, integrating it with LangChain."
The course is structured as a self-paced lab in Google Cloud.
"I appreciate that it's a self-paced lab, allowing me to learn and build at my own speed."
"I'm looking for a hands-on lab experience rather than purely theoretical lectures."
"I expect to complete this directly in the Google Cloud console, following the guided steps."
Develop a practical LLM-powered chat application.
"I expect to gain practical experience building a real-world application with LLMs."
"I anticipate creating an interactive solution in a deployed environment using cutting-edge tech."
"I look forward to applying RAG concepts directly to build a functional chat application."
Likely requires foundational knowledge in Python, SQL, and ML/AI.
"I would expect to have prior experience with Python programming and cloud concepts before starting."
"I anticipate needing a basic understanding of LLMs and database interactions for this lab."
"I understand this is a practical lab, not a beginner's introduction to AI or programming."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Build an LLM and RAG-based Chat Application using AlloyDB and LangChain with these activities:
Review basic programming concepts
Strengthen your programming foundation before starting the course.
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Show steps
  • Review a programming tutorial
  • Complete a few practice exercises
Compile a glossary of key terms and concepts
Enhance your understanding by organizing and defining key terms.
Show steps
  • Identify key terms and concepts
  • Find definitions for each term
  • Organize the terms into a glossary
Read "Designing Data-Intensive Applications"
Provides a comprehensive overview of designing and building scalable, reliable data-intensive applications on Google Cloud.
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  • Read chapters 1-3
  • Complete the exercises in chapters 1-3
  • Summarize the key concepts covered in chapters 1-3
Six other activities
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Show all nine activities
Read "Designing Data-Intensive Applications"
Provides a comprehensive overview of designing and building scalable, reliable data-intensive applications on Google Cloud.
View Secret Colors on Amazon
Show steps
  • Read chapters 1-3
  • Complete the exercises in chapters 1-3
  • Summarize the key concepts covered in chapters 1-3
Join a study group or forum
Connect with other students to discuss course material and share knowledge.
Show steps
  • Find a study group or forum
  • Participate in discussions
  • Ask and answer questions
Follow the Google Cloud Build Tutorial
Gain hands-on experience setting up and using Google Cloud Build for CI/CD.
Show steps
  • Follow the tutorial steps
  • Build a sample application using Cloud Build
Solve practice problems on Cloud Functions
Strengthen your understanding of Cloud Functions by solving practice problems.
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  • Find practice problems online
  • Solve the practice problems
  • Review your answers for accuracy
Write a blog post on a topic covered in the course
Reinforce your understanding by writing and sharing your knowledge with others.
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  • Choose a topic from the course material
  • Research and gather information on the topic
  • Write a blog post that explains the topic in a clear and concise manner
  • Publish your blog post online
Build a simple web application using Cloud Run
Apply your knowledge in a practical setting by building a functional web application.
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Show steps
  • Design the application
  • Create the application code
  • Deploy the application to Cloud Run
  • Test the application

Career center

Learners who complete Build an LLM and RAG-based Chat Application using AlloyDB and LangChain will develop knowledge and skills that may be useful to these careers:

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Course image
Effort
60 minutes
Level
Introductory
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Builds a strong foundation for beginners in using LLM and RAG for interactive applications
Taught by Google Cloud Training, who are recognized for their work in cloud computing
Explores concepts and tools that are highly relevant to industry
Offers hands-on labs and interactive materials that enhance learning
Requires prior knowledge in software development
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