Mastering Generative AI: Agents with RAG and LangChain from edX

The demand for technical gen AI skills is rocketing. AI engineers with competencies in large language models (LLMs), and related methodologies and frameworks such as RAG and LangChain, are highly sought-after. This Mastering Generative AI - Agents with RAG and LangChain course builds the job-ready skills you need to catch the eye of an employer.

During the course, you’ll explore retrieval-augmented generation (RAG), prompt engineering, and LangChain. You’ll build your understanding of RAG, its applications, and its process, along with encoders, their tokenizers, and the Facebook AI similarity search (FAISS) library. Further You’ll learn how to apply in-context learning and prompt engineering to design and refine prompts for accurate responses. Plus, you’ll dive into the world of LangChain tools, components, and chat models, and work with LangChain to simplify the application development process using LLMs.

Throughout the course, you’ll get hands-on practice in online labs developing applications using integrated LLM, LangChain, and RAG technologies. Plus, you’ll complete a real-world project you can talk about in interviews.

If you’re looking to build job-ready skills in RAG and LangChain that employers are looking for, ENROLL TODAY and get ready to power up your resume!

What's inside

Learning objectives

In-demand job-ready skills businesses need for building ai agents using rag and langchain in just 8 hours.
How to apply fundamentals of in-context learning and advanced methods of prompt engineering to enhance prompt design.

Langchain concepts, tools, components, chat models, chains, and agents.
How to integrate rag, pytorch, hugging face, llms, and langchain technologies with gen ai applications.

In-demand job-ready skills businesses need for building ai agents using rag and langchain in just 8 hours.
How to apply fundamentals of in-context learning and advanced methods of prompt engineering to enhance prompt design.
Langchain concepts, tools, components, chat models, chains, and agents.
How to integrate rag, pytorch, hugging face, llms, and langchain technologies with gen ai applications.

Syllabus

Lesson 0: Welcome

Video: Course Introduction

Reading: Professional Certificate Overview

Reading: General Information

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Develops skills in retrieval-augmented generation (RAG), prompt engineering, and LangChain, which are highly sought-after in the field of AI engineering

Provides hands-on experience with online labs, allowing learners to develop applications using integrated LLM, LangChain, and RAG technologies

Includes a real-world project, providing learners with practical experience to discuss in interviews and showcase their skills

Explores encoders, their tokenizers, and the Facebook AI similarity search (FAISS) library, which are essential tools for RAG implementation

Requires learners to integrate RAG, PyTorch, Hugging Face, and LLMs, which may require familiarity with these technologies

Presented by IBM, a company recognized for its contributions to artificial intelligence and machine learning

Reviews summary

Rag and langchain practical guide

According to learners, this course provides a solid foundation for understanding RAG and LangChain, focusing on practical application through hands-on labs. Students particularly highlight the clarity of explanations for complex topics and the usefulness of the guided project in solidifying concepts. While generally very positively received, a few mention needing prior Python knowledge and occasionally encountering minor issues with lab environments, which seem to be addressed over time.

Requires basic Python programming knowledge.

"Would be better if there was a clearer mention of required Python proficiency beforehand."

"Assumes you have basic Python knowledge, which was fine for me but might be tough for others."

"Some sections move fast if you're not comfortable with Python coding."

"A bit challenging if you don't have some Python background before starting."

Content is up-to-date and highly relevant.

"The course content is very current, which is important in this fast-moving field."

"Glad to see material on RAG and LangChain, which are highly relevant right now."

"Covers modern techniques and libraries used in Gen AI agent development."

"The course is very updated and useful, relevant to current AI trends."

Guided project reinforces learning effectively.

"The guided project was incredibly valuable and helped tie everything together."

"Working on the guided project really solidified my understanding."

"The project was challenging but very rewarding. Felt like I learned a lot from it."

"Final project was a great way to apply the learned concepts."

Provides a strong foundational understanding.

"Provides a solid foundation for RAG and LangChain. Great starting point."

"An excellent introduction to building AI agents with these technologies."

"This is a good intro course into the LangChain and RAG world."

"Perfect for getting a foundational grasp of how RAG and LangChain fit together."

Teaches in-demand skills for AI agents.

"This course is highly relevant for anyone looking to build AI agents or work with LLMs professionally."

"Gave me the job-ready skills I needed to start applying for roles involving Gen AI."

"The content directly aligns with current industry needs in AI and LLMs."

"Very good course to learn practical skills of LangChain and RAG on building Gen AI agents."

"Highly recommended for professionals needing to quickly get up to speed on RAG and LangChain."

Complex topics explained clearly and concisely.

"The instructor explains the concepts clearly and concisely. Complex topics are broken down well."

"Really helped solidify my understanding of RAG and LangChain principles through clear explanations."

"I appreciated how the material was presented; it made understanding RAG and LangChain much easier than expected."

"Clear and easy to follow, even for relatively new concepts. The pacing was just right."

"The videos were informative and the explanations were spot-on, easy to digest the content."

Course excels with practical labs and projects.

"The hands-on coding and projects are the strongest part of the course for me, especially the guided project."

"I really enjoyed the labs; they helped cement the concepts discussed in the lectures. Great practical application."

"The labs were quite useful and practical, giving me a chance to implement RAG and LangChain quickly."

"Good practical labs to get you started with LangChain and RAG."

"The hands on experience is invaluable and makes this course a stand out among others."

Occasional minor technical issues reported.

"Occasionally encountered small issues with the lab environment setup."

"Some labs needed minor troubleshooting, but nothing major."

"A few dependencies seemed slightly outdated in the lab setup, but solvable."

"Had a couple of hiccups getting the labs running, but support helped quickly."

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 Mastering Generative AI: Agents with RAG and LangChain with these activities:

Review Hugging Face Transformers

Show steps

Solidify your understanding of Hugging Face Transformers, a key component used in the RAG implementation within the course.

Browse courses on Hugging Face Transformers

Show steps

Review the Hugging Face documentation on transformers.
Complete a basic tutorial on using transformers for text classification.

Read 'Natural Language Processing with Transformers'

Show steps

Gain a deeper understanding of the transformer models that underpin much of the generative AI landscape.

View Natural Language Processing with Transformers,... on Amazon

Show steps

Read the chapters related to transformer architecture and attention mechanisms.
Experiment with the code examples provided in the book.

Practice Prompt Engineering

Show steps

Improve your prompt engineering skills through targeted exercises, which are crucial for effective interaction with LLMs.

Browse courses on Prompt Engineering

Show steps

Experiment with different prompt structures and phrasing.
Evaluate the output of the LLM for different prompts.
Refine prompts based on the LLM's responses.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Read 'Generative AI with LangChain'

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Deepen your knowledge of LangChain with a book dedicated to its practical applications in generative AI.

View Putting Knowledge to Work on Amazon

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Read the chapters related to RAG and agent design.
Implement the code examples provided in the book.

Build a Q&A Bot with RAG and LangChain

Show steps

Apply your knowledge by building a practical Q&A bot, integrating RAG, LangChain, and a chosen LLM.

Show steps

Choose a dataset of documents for the bot to answer questions about.
Implement the RAG pipeline using LangChain.
Integrate an LLM to generate answers.
Test and refine the bot's performance.

Create a Blog Post on LangChain Agents

Show steps

Solidify your understanding of LangChain agents by explaining their functionality and use cases in a blog post.

Show steps

Research different types of LangChain agents and their capabilities.
Write a clear and concise explanation of how LangChain agents work.
Provide examples of real-world applications of LangChain agents.
Publish the blog post on a platform like Medium or your personal website.

Contribute to a LangChain Project

Show steps

Enhance your skills and contribute to the community by contributing to an open-source LangChain project.

Browse courses on LangChain

Show steps

Find an open-source LangChain project on GitHub.
Identify an issue or feature to work on.
Submit a pull request with your changes.

Career center

Learners who complete Mastering Generative AI: Agents with RAG and LangChain will develop knowledge and skills that may be useful to these careers:

Generative AI Developer

A Generative AI Developer builds applications that can generate content, which may include text, images, or code. This career path is closely aligned with the focus of this course, as generative AI development requires a strong understanding of prompt engineering, LangChain, and RAG. This course offers practical experience in using these tools, which can be directly applied by a Generative AI Developer. For a Generative AI Developer, working with LLMs is crucial, and the course provides hands-on practice integrating LLMs with other technologies. The real-world project and online labs are particularly relevant for a Generative AI Developer, providing directly applicable experience.

See salaries and explore the career path for Generative AI Developer

Artificial Intelligence Engineer

An Artificial Intelligence Engineer develops and implements AI solutions, often working with large language models. This career involves building, training, and deploying AI models, and this course helps build a foundation in employing retrieval-augmented generation, prompt engineering, and LangChain, critical tools for developing advanced AI applications. A learner interested in this career should focus on mastering prompt engineering and the use of LangChain to simplify the application development process. Learning how to integrate RAG, PyTorch, Hugging Face, LLMs, and LangChain technologies provides a concrete advantage as an Artificial Intelligence Engineer.

See salaries and explore the career path for Artificial Intelligence Engineer

AI Application Developer

An AI Application Developer focuses on integrating AI models into software applications, a role that is increasingly important. This career requires skills in using tools like LangChain and building with LLMs, and this course helps build those job-ready skills. For an AI Application Developer, the use of RAG to enhance responses is fundamental and this course offers practical labs with RAG, as well as training in Langchain. The course also emphasizes practical experience, which is useful for those developing AI applications. AI Application Developers benefit from the full range of skills covered in this course.

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Machine Learning Engineer

A Machine Learning Engineer builds and manages machine learning models, typically requiring skills in areas like model development and deployment, which this course directly supports. This role can involve using advanced techniques with Language Models and related methodologies and frameworks such as RAG and LangChain. It may be helpful to a Machine Learning Engineer to learn with hands-on practice with online labs developing applications using integrated LLM, LangChain, and RAG technologies, as this course provides. A learner aiming for a Machine Learning Engineer role should pay special attention to the LangChain tools, components, and chat models.

See salaries and explore the career path for Machine Learning Engineer

Natural Language Processing Engineer

A Natural Language Processing Engineer focuses on enabling machines to understand and process human language. This career path often requires skills in prompt engineering and the use of LLMs, which this course provides. A Natural Language Processing Engineer needs to understand how to apply in-context learning to design prompts for accurate responses and this course has a direct focus in that area. Because this role involves the design of advanced language understanding systems, a learner seeking this career path will want to get hands-on experience with LangChain, a vital skill. The use of RAG and LangChain can directly translate into a competitive edge in the field of natural language processing.

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Data Scientist

A Data Scientist analyzes data to extract insights, and increasingly, this role involves using generative AI. Understanding how to work with LangChain, RAG, and prompt engineering is becoming increasingly relevant for a Data Scientist. A Data Scientist may find the ability to refine prompts for accurate responses invaluable, gaining skills in in-context learning directly from this course. This course provides hands-on experience in integrating AI technologies that are useful in data science. The skills taught will enable a Data Scientist to handle more complex datasets.

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Research Scientist

A Research Scientist in the field of artificial intelligence conducts research to advance the field, which may include exploring new methods for using large language models. This career path is supported by this course's focus on RAG, LangChain, and prompt engineering. A Research Scientist can leverage the understanding of encoders, tokenizers, and the FAISS library that this course delivers. This course may be useful in providing hands-on experience that could inspire a Research Scientist to further explore innovative solutions in AI. It will help a Research Scientist stay on the cutting edge of AI developments.

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Solutions Architect

A Solutions Architect designs and guides the implementation of technology solutions for businesses, and increasingly, this includes generative AI. This career role can benefit from an understanding of RAG and LangChain, as provided in this course. The real-world project offered by the course can help a Solutions Architect understand how to integrate these technologies into a larger system. This course may be helpful in showcasing how different AI solutions can solve various problems. The hands-on experience with the integrated technologies can make a Solutions Architect more effective.

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AI Consultant

An AI Consultant advises clients on how to implement AI solutions and requires skills in various AI technologies. This career role matches up with the core focus of the course, which is to enhance skills in using LLMs with RAG and LangChain. This course may be useful in helping an AI Consultant understand in-context learning and prompt engineering to help clients refine the responses of their systems. The course's practical labs on applying gen AI technologies translate directly to the skills needed by an AI Consultant. The material serves as a foundation for a AI Consultant's advisory work.

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Software Engineer

A Software Engineer designs and develops software systems and may find the concepts from this course useful. As this course teaches the use of cutting-edge AI frameworks and tools like LangChain, it may be useful to the Software Engineer who is working on applications that require use of generative AI. The application development process using LLMs shown in this course is valuable for a Software Engineer who wants to create such systems. This course's focus on practical application helps Software Engineers who need experience integrating AI into their projects. A Software Engineer with knowledge of RAG and LangChain has an advantage.

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Data Analyst

A Data Analyst analyzes data to identify trends, and increasingly this involves using AI tools. A Data Analyst will see value in this course's focus on prompt engineering, which can help improve how they query data. A Data Analyst may find the skills learned using RAG to enhance responses very useful. While this role is not directly focused on AI, the skills offered can help Data Analysts stay relevant in a rapidly evolving industry. While not central to a Data Analyst's work, it may be useful in helping Data Analysts use AI to analyze data more efficiently.

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Technical Project Manager

A Technical Project Manager oversees technical projects, and this role increasingly involves some understanding of AI tools. While a Technical Project Manager does not typically develop the systems themselves, a baseline understanding of how they are constructed, using LangChain for example, is helpful. In this course, concepts such as RAG and prompt engineering are explained which may be useful for someone who needs to work with development teams using these tools. This course can give a Technical Project Manager a greater understanding of the tools and processes that development teams use. This may be useful for those who manage projects involving AI.

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Technical Writer

A Technical Writer creates documentation for technical products, and this role can benefit from an understanding of current AI technologies. This course may be useful for a Technical Writer as they need to understand the tech they are writing about. Familiarity with concepts like RAG and LangChain may be useful. While this course does not focus specifically on writing skills, it may give a Technical Writer hands on technical experience with the subject matter they cover. Because of the practical labs, a Technical Writer may find it easier to explain these technologies.

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IT Specialist

An IT Specialist manages and maintains an organization's IT infrastructure and this role may involve adopting AI solutions. Familiarity with AI tools like LangChain and RAG may be useful for an IT Specialist who needs to integrate such tools into existing systems. It may be helpful to an IT Specialist to understand the practical uses of these AI technologies, such as the integration of RAG, PyTorch, Hugging Face, LLMs, and LangChain technologies with gen AI applications. While this course may not directly support every function of this role, it may be helpful to IT Specialists who want to stay up-to-date with modern technologies.

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Business Analyst

A Business Analyst identifies business needs and proposes solutions, increasingly involving AI. A Business Analyst can use the knowledge gained in using generative AI tools, like those covered in this course, to better understand project solutions involving AI. A Business Analyst may find prompt engineering and the use of LangChain useful as they work on defining the needs for systems that use these tools. While a Business Analyst would not directly use the LLMs themselves, it may be helpful to understand how they can solve business needs. This course may be useful to a Business Analyst for this reason.

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Mastering Generative AI

Agents with RAG and LangChain

What's inside

Learning objectives

Syllabus

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Reviews summary

Rag and langchain practical guide

Activities

Career center

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