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March 4, 2024
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This course is part of multiple programs.
Instructors: Kang Wang
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Recommended experience
Intermediate level
To successfully complete this project, it is recommended that learners complete all courses in Generative AI Engineering with LLMs specialization.
(35 reviews)
Recommended experience
Intermediate level
To successfully complete this project, it is recommended that learners complete all courses in Generative AI Engineering with LLMs specialization.
Gain practical experience building your own real-world gen AI application that you can talk about in interviews.
Get hands-on using LangChain to load documents and apply text splitting techniques with RAG and LangChain to enhance model responsiveness.
Create and configure a vector database to store document embeddings and develop a retriever to fetch document segments based on queries.
Set up a simple Gradio interface for model interaction and construct a QA bot using LangChain and an LLM to answer questions from loaded documents.
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October 2024
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Get ready to put all your gen AI engineering skills into practice! This guided project will test and apply the knowledge and understanding you’ve gained throughout the previous courses in the program. You will build your own real-world gen AI application.
During this course, you will fill the final gaps in your knowledge to extend your understanding of document loaders from LangChain. You will then apply your new skills to uploading your own documents from various sources. Next, you will look at text-splitting strategies and use them to enhance model responsiveness. Then, you will use watsonx to embed documents, a vector database to store document embeddings, and LangChain to develop a retriever to fetch documents. As you work through your project, you will also implement RAG to improve retrieval, create a QA bot, and set up a simple Gradio interface to interact with your models. By the end of the course, you will have a hands-on project that provides engaging evidence of your generative AI engineering skills that you can talk about in interviews. If you’re ready to add some real-world experience to your portfolio, enroll today and fuel your AI engineering career.
In this module, you will learn all about document loaders from LangChain and then use that knowledge to load your document from various sources. You will also explore the various text splitting strategies with RAG and LangChain and apply them to enhance model responsiveness. Hands-on labs will provide you an opportunity to practice loading documents as well as implement the text-splitting techniques you have learned.
3 videos4 readings2 assignments3 app items1 plugin
In this module, you will learn how to store embeddings using a vector store and how to use Chroma DB to save embeddings. You’ll gain insights into LangChain retrievers like the Vector Store-Based, Multi-Query, Self-Query, and Parent Document Retriever. In hands-on labs, you’ll prepare and preprocess documents for embedding and use watsonx.ai to generate embeddings for your documents. You’ll use vector databases such as Chroma DB and FAISS to store embeddings generated from textual data using LangChain. Finally, you’ll use various retrievers to efficiently extract relevant document segments from text using LangChain.
3 videos1 reading2 assignments3 app items2 plugins
In this module, you will learn how to implement RAG to improve retrieval. You will become familiar with Gradio and how to set up a simple Gradio interface to interact with your models. You will also learn how to construct a QA bot to answer questions from loaded documents using LangChain and LLMs. Using hands-on labs, you will have the opportunity to practice setting up a Gradio interface, as well as constructing a QA bot. In the final project, you will build an AI application using RAG and LangChain.
1 video4 readings3 assignments1 peer review2 app items4 plugins
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
Instructor ratings
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
At IBM, we know how rapidly tech evolves and recognize the crucial need for businesses and professionals to build job-ready, hands-on skills quickly. As a market-leading tech innovator, we’re committed to helping you thrive in this dynamic landscape. Through IBM Skills Network, our expertly designed training programs in AI, software development, cybersecurity, data science, business management, and more, provide the essential skills you need to secure your first job, advance your career, or drive business success. Whether you’re upskilling yourself or your team, our courses, Specializations, and Professional Certificates build the technical expertise that ensures you, and your organization, excel in a competitive world.
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Reviewed on Dec 18, 2024
The best of the one of AI foundation courser, Thanks a lot, only this course give code detail material, really learned a lot, Super, Bravo!
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This course is suitable for those interested in AI engineering and includes training, developing, fine-tuning, and deploying large language models (LLMs). It is the ideal project course for learners who have completed the other courses in the Specialization title: Generative AI Engineering with LLMs.
Existing and aspiring data scientists, AI engineers, and machine learning engineers will benefit greatly from completing this project.
With 3–4 hours of study per week, you can complete this course and the guided project in 3 weeks. If you are able to put in more time per week, you can complete it a lot faster!
This course is intermediate level, so you must have basic knowledge of Python. Familiarity with LLMs, LangChain, and RAG would be an added advantage.However, to get the most out of this course, we recommend that you complete all the other courses in the IBM Generative AI Engineering with LLMs specialization.
This course is part of the Generative AI Engineering with LLMs specialization. When you complete this course and the guided project, you will have the hands-on skills and confidence to take on jobs such as AI engineer, NLP engineer, machine learning engineer, deep learning engineer, data scientist, or software developer seeking to work with LLMs.
Only a modern web browser is required to complete this course and all hands-on labs. You will be provided access to cloud-based environments to complete the labs at no charge.
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
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