Build Chat Applications with OpenAI and LangChain from Udemy

Are you an aspiring AI engineer excited to integrate AI into your product?

Are you thrilled about the breakthroughs in the field of AI?

Or maybe you’re eager to learn this new and exciting LangChain framework everyone’s talking about.

If yes, then you’ve come to the right place.

Why should you consider taking this LangChain course?

In this Build Chat Applications with OpenAI and LangChain course, we’ll explore the increasingly popular LangChain Python library to develop engaging chatbot applications.

With detailed, step-by-step guidance, you will use OpenAI’s API key to access their powerful large language models (LLMs). Once we have access to foundational models, we'll utilize LangChain and its integrations to create compelling prompts, add memory, input external data, and link it to third-party tools.

LangChain's integration with third-party tools distinguishes it by enabling connections to various language models and loading documents in multiple formats. It also allows for selecting suitable embedding models, storing embeddings in a vector store, and linking to search engines, code interpreters, and tools like Wikipedia, GitHub, Gmail, and more.

None of this would be possible without mastering the LangChain Expression Language (LCEL)—essential for developing stateful, context-aware reasoning chatbots. These chatbots remember past conversations, answer questions about unseen data, and tackle more complex problems.

Additionally, we’ll spend much of our time discussing the state-of-the-art Retrieval Augmented Generation (RAG), both theoretically and practically. This technique allows LLM-powered applications to analyze and answer questions about information outside their training data. Ultimately, we’ll create a chatbot that answers students’ questions on courses from the 365 library.

What skills do you gain?

- Integrate existing applications with powerful LLMs.

- Connect to OpenAI’s language and embedding models using an OpenAI API key.

- Develop prompt engineering techniques to enhance AI response performance and relevance.

- Implement RAG to enrich your AI-driven product with a knowledge base.

- Master the LCEL protocol—essential for developing applications with the LangChain Python library.

- Connect external tools to your LLM-powered application.

- Understand the mechanics behind agents and agent executors.

Enhance your career prospects with rare and highly sought-after AI Engineering skills by enrolling in this LangChain and OpenAI course.

Click ‘Buy Now’ and acquire real-world AI engineer skills today.

What's inside

Learning objectives

Master langchain to seamlessly integrate existing applications with potent large language models (llms)
Learn to connect to openai’s language and embedding models
Develop prompt engineering skills that improve performance and relevance of ai responses

Apply the state-of-the-art retrieval augmented generation (rag) technique to empower your ai-driven product with a knowledge base
Leverage ai to open up endless opportunities for your organization
Enhance your career prospects with rare and highly sought-after ai engineering skills

Master langchain to seamlessly integrate existing applications with potent large language models (llms)
Learn to connect to openai’s language and embedding models
Develop prompt engineering skills that improve performance and relevance of ai responses
Apply the state-of-the-art retrieval augmented generation (rag) technique to empower your ai-driven product with a knowledge base
Leverage ai to open up endless opportunities for your organization
Enhance your career prospects with rare and highly sought-after ai engineering skills

Syllabus

In this introductory section, we outline the agenda and prerequisites for the LangChain course. We provide an overview of what LangChain represents and how it’s been used in real-life use cases.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Covers LangChain Expression Language (LCEL), which is essential for developing stateful, context-aware reasoning chatbots that remember past conversations and tackle complex problems

Explores Retrieval Augmented Generation (RAG), a state-of-the-art technique that allows LLM-powered applications to analyze and answer questions about information outside their training data

Teaches prompt engineering techniques to enhance AI response performance and relevance, which is a highly sought-after skill in the field of AI engineering

Requires an OpenAI API key, which may incur costs depending on usage and could be a barrier for some learners without access to sufficient funds

Focuses on integrating existing applications with Large Language Models (LLMs), which is useful for engineers looking to enhance their current projects with AI capabilities

Utilizes LangChain's integration with third-party tools, enabling connections to various language models and loading documents in multiple formats, which expands the possibilities for chatbot applications

Reviews summary

Build chat applications with openai and langchain

According to learners, this course offers a practical and hands-on approach to building chat applications using OpenAI and LangChain. Many found the lectures clear and well-explained, particularly appreciating the focus on Retrieval Augmented Generation (RAG) and LangChain Expression Language (LCEL). While the course provides a strong foundation, some reviewers noted it requires a solid understanding of Python and basic LLM concepts beforehand. The coding demonstrations and labs were frequently highlighted as a key strength, enabling students to apply concepts immediately. A few mentioned that the field is rapidly evolving, suggesting the need for ongoing learning beyond the course.

Content may need occasional updates.

"Since the LLM field moves so fast, some parts might become outdated quickly, but the core concepts are solid."

"It's a dynamic area, so expect to do some additional reading to stay current after the course."

"The course provides a great snapshot, but continuous learning is required as LangChain and OpenAI evolve."

Covers core, in-demand LangChain topics.

"The sections on RAG and LCEL were particularly valuable and are highly relevant in today's LLM landscape."

"Getting a solid grasp of LCEL from this course was a major plus; it's crucial for building complex chains."

"The deep dive into RAG was fantastic; I feel confident applying it to my own data now."

"This course gives you the necessary foundation in key LangChain components like RAG and LCEL."

Well-structured lessons, easy to follow.

"The instructor explains complex topics like LCEL and RAG in a very clear and understandable way."

"The lectures were concise and straight to the point, making it easy to absorb the information."

"Everything was explained thoroughly, making even difficult concepts approachable for learners."

"I found the step-by-step guidance particularly helpful for grasping the material."

Strong focus on practical coding and examples.

"The hands-on coding and projects are the strongest part of the course for me, enabling me to build practical applications."

"I really appreciated the practical exercises and the final project, which solidified my understanding of LangChain concepts."

"The course is very practical, focusing on writing code and building stuff, which is exactly what I needed."

"It's great that the course provides concrete code examples you can run yourself to see how things work."

Needs prior Python/LLM knowledge.

"While the course is good, a solid understanding of Python is definitely necessary to keep up."

"I'd recommend having some basic familiarity with large language models before taking this course."

"It assumes you're comfortable with coding in Python and setting up environments."

"Not suitable for absolute beginners; prior programming experience is a must."

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 Chat Applications with OpenAI and LangChain with these activities:

Review Python Fundamentals

Show steps

Strengthen your Python foundation to better understand LangChain's Python library and code examples.

Browse courses on Python Basics

Show steps

Review basic data types, control flow, and functions in Python.
Practice writing simple Python scripts.

Read 'Building LLM Applications with LangChain'

Show steps

Supplement the course material with a dedicated book on LangChain to gain a deeper understanding of the framework.

View Alter Ego: A Novel on Amazon

Show steps

Read the book cover to cover.
Experiment with the code examples provided in the book.

Experiment with Prompt Engineering Techniques

Show steps

Refine your prompt engineering skills by experimenting with different prompts and parameters.

Show steps

Try different prompt templates and strategies.
Adjust the temperature and max tokens parameters to see how they affect the output.
Analyze the results and identify the most effective prompts.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Build a Simple Question Answering Chatbot

Show steps

Apply your knowledge by building a chatbot that answers questions based on a specific document or dataset.

Show steps

Choose a dataset or document to use as the knowledge base.
Implement document loading, splitting, and embedding using LangChain.
Create a retrieval chain to answer questions based on the knowledge base.

Write a Blog Post on RAG

Show steps

Solidify your understanding of Retrieval Augmented Generation (RAG) by explaining the concept in a blog post.

Show steps

Research and understand the different components of RAG.
Write a clear and concise explanation of RAG, including its benefits and limitations.
Provide examples of how RAG can be used in real-world applications.

Read 'Generative AI with LangChain'

Show steps

Expand your knowledge of generative AI and how LangChain can be used to build generative applications.

View Alter Ego: A Novel on Amazon

Show steps

Read the book and take notes on the key concepts.
Try out the code examples and adapt them to your own projects.

Contribute to LangChain

Show steps

Deepen your understanding of LangChain by contributing to the open-source project.

Show steps

Explore the LangChain GitHub repository and identify areas where you can contribute.
Fix bugs, write documentation, or add new features.
Submit your contributions and participate in the code review process.

Career center

Learners who complete Build Chat Applications with OpenAI and LangChain will develop knowledge and skills that may be useful to these careers:

AI Prompt Engineer

An AI Prompt Engineer specializes in crafting effective prompts for large language models to get desired outputs. This course helps build a foundation in prompt engineering techniques that improve the performance and relevance of AI responses using LangChain. These skills are directly transferable to the role of an AI Prompt Engineer, enhancing their ability to design prompts that elicit specific and accurate information from LLMs. The course also delves into Retrieval Augmented Generation, which is crucial for prompt engineers working with information outside an LLM's original training dataset.

See salaries and explore the career path for AI Prompt Engineer

Chatbot Developer

A Chatbot Developer designs and implements conversational AI systems. This course directly addresses the core skills needed for this role, as it focuses on building chat applications using OpenAI and LangChain. The course covers connecting to OpenAI's language models, developing prompt engineering techniques, and implementing Retrieval Augmented Generation to enable chatbots to answer questions based on external data. Anyone wanting to become a Chatbot Developer will find this course invaluable, especially with its coverage of LangChain Expression Language and the creation of stateful, context-aware reasoning chatbots.

See salaries and explore the career path for Chatbot Developer

AI Solutions Architect

The AI Solutions Architect designs and oversees the implementation of AI-driven solutions for businesses. This course provides practical experience in integrating large language models into existing applications and connecting them to various third-party tools using LangChain. This exposure mirrors the responsibilities of an AI Solutions Architect, especially concerning the architecture's ability to incorporate new data sources and tools. The course's exploration of Retrieval Augmented Generation is particularly relevant, as it offers a way to enhance AI-driven products with a knowledge base, a common requirement in AI solution design.

See salaries and explore the career path for AI Solutions Architect

Machine Learning Engineer

A Machine Learning Engineer develops and deploys machine learning models. This course, with its focus on LangChain and OpenAI, helps build skills in integrating large language models into applications. Machine Learning Engineers can leverage this knowledge to build more sophisticated AI-powered features. The course also introduces Retrieval Augmented Generation, a technique that allows LLMs to analyze and answer questions about information outside their training data, increasing the power of machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Natural Language Processing Engineer

A Natural Language Processing Engineer develops algorithms that enable computers to understand and process human language. Because this course emphasizes LangChain, which facilitates the interconnection of external tools and language models, Natural Language Processing Engineers may find it useful. LangChain enables connections to various language models and loading documents in multiple formats. It also allows for selecting suitable embedding models, storing embeddings in a vector store, and linking to search engines, code interpreters, and tools like Wikipedia and GitHub.

See salaries and explore the career path for Natural Language Processing Engineer

Data Scientist

Data Scientists analyze data to extract meaningful insights. This course may be helpful, as it provides exposure to LangChain and OpenAI, which can extend a Data Scientist's toolkit for working with text-based data. The course's focus on Retrieval Augmented Generation can be particularly useful for answering questions about information outside the training data of language models. Data Scientists can also learn how to integrate custom data into large language models.

See salaries and explore the career path for Data Scientist

Software Engineer

Software Engineers design, develop, and test software applications. This course may be useful to Software Engineers, especially those working on AI-powered features. The course focuses on using LangChain with OpenAI to integrate large language models into existing applications. The skills learned can enhance the capabilities of their projects and add value to their organizations. The course provides Software Engineers exposure to cutting edge concepts in AI application development.

See salaries and explore the career path for Software Engineer

Technical Lead

A Technical Lead manages a team of engineers and guides technical direction. This course may offer value to a Technical Lead, especially in organizations adopting AI solutions. An understanding of LangChain and OpenAI, as highlighted in the course, may assist a Technical Lead in guiding the team's efforts in integrating large language models into existing systems. The course can provide an overview of state-of-the-art techniques in AI application development.

See salaries and explore the career path for Technical Lead

AI Product Manager

An AI Product Manager defines the vision and strategy for AI-powered products. This course may be beneficial for understanding the technical landscape of AI application development using LangChain and OpenAI. The course provides insights into the capabilities of language models and how they can be integrated with external tools. This is invaluable for an AI Product Manager, especially when strategizing new features and evaluating the feasibility of AI-driven solutions.

See salaries and explore the career path for AI Product Manager

Business Analyst

Business Analysts identify business needs and propose solutions, often involving technology. This course may be useful for Business Analysts wanting to stay current with AI trends. The course provides exposure to LangChain and OpenAI, illustrating how large language models can be integrated into various applications. This knowledge can help Business Analysts identify opportunities to leverage AI for business process improvements and new product innovations.

See salaries and explore the career path for Business Analyst

Data Analyst

Data Analysts interpret data and transform it into actionable insights. This course may be useful for Data Analysts interested in expanding their skillset to include natural language processing techniques. The course provides an introduction to LangChain and OpenAI, tools that can be used to analyze and extract information from text-based data. Data Analysts can leverage these tools to gain deeper insights from unstructured text data sources.

See salaries and explore the career path for Data Analyst

Solutions Architect

A Solutions Architect designs and implements solutions to complex business problems. This course may be useful for those interested in integrating AI into their solutions. The course provides practical experience in integrating large language models into existing applications and connecting them to various third-party tools using LangChain. Solutions Architects need to ensure that their architectures can incorporate new data sources and tools.

See salaries and explore the career path for Solutions Architect

Research Scientist

Research Scientists conduct research and development in various fields. This course may be useful, especially for those working on natural language processing or artificial intelligence projects. The course can help a Research Scientist, as it covers integrating large language models into existing applications and connecting them to various third-party tools using LangChain. Research Scientists need to be experts in state-of-the-art techniques.

See salaries and explore the career path for Research Scientist

Technical Writer

A Technical Writer creates documentation for technical products and services. To excel as a Technical Writer, they must possess familiarity with current technology, including how users are integrating AI into their applications. This course provides Technical Writers with exposure to LangChain and OpenAI. It provides knowledge about integrating large language models into existing applications and connecting them to various third-party tools.

See salaries and explore the career path for Technical Writer

AI Consultant

AI Consultants advise organizations on how to implement AI solutions. This course can help AI Consultants stay current with the latest developments in integrating large language models into applications. The course provides practical experience with LangChain and OpenAI, which are increasingly popular tools for building AI-powered features. AI Consultants must maintain an awareness of the current industry landscape.

See salaries and explore the career path for AI Consultant

Reading list

We've selected one books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Build Chat Applications with OpenAI and LangChain.

Alter Ego

Save

Provides a comprehensive guide to building applications using LangChain. It covers core concepts, advanced techniques, and real-world examples. It serves as a valuable reference for understanding and implementing LangChain features discussed in the course. This book will help you build a strong foundation in LangChain and LLMs.

Alter Ego: A Novel

Paperback

Build Chat Applications with OpenAI and LangChain

What's inside

Learning objectives

Syllabus

Traffic lights

Save this course

Reviews summary

Build chat applications with openai and langchain

Activities

Career center

Reading list

Share

Similar courses