Generative AI Architecture and Application Development from Coursera

Welcome to the 'Generative AI Architecture and Application Development' course, your gateway to mastering the advanced landscape of Generative AI and their transformative applications across industries.

In this immersive course, participants will journey through the comprehensive world of LLMs, gaining insights into their foundational architecture, training methodologies, and the spectrum of applications they empower. By the end of this course, you will be equipped with the knowledge to:

- Grasp the architectural nuances and training intricacies of Large Language Models, setting a solid foundation for understanding their capabilities and limitations.

- Apply LLMs to a variety of tasks including search, prediction, and content generation, showcasing the versatility and power of generative AI in solving complex challenges.

- Leverage the LangChain library to streamline the development of LLM applications, enhancing efficiency and innovation in your projects.

- Explore advanced data interaction techniques using Retrieval-Augmented Generation (RAG), enriching the functionality and intelligence of LLM outputs.

- Critically assess LLM performance, employing robust evaluation strategies to ensure your AI solutions are both effective and ethically aligned.

This course is designed for a wide audience, from AI enthusiasts and software developers to data scientists and technology strategists seeking to deepen their expertise in generative AI and LLMs. Whether you are new to the field or looking to expand your knowledge, this course offers a structured path to enhancing your proficiency in leveraging LLMs for innovative solutions.

A basic understanding of artificial intelligence concepts and familiarity with programming concepts are beneficial but not mandatory to complete this course.

Embark on this educational journey to unlock the full potential of Large Language Models and Generative AI, propelling your professional growth and positioning you at the forefront of AI innovation.

What's inside

Syllabus

Generative AI with LLMs

In this module, learners will embark on an exploration of Large Language Models (LLMs), starting with the essentials of pre-training and scaling, to understand how model size and data quality influence generalization capabilities. The journey advances with hands-on fine-tuning practices, teaching learners to adapt LLMs for specific tasks while maintaining a broad knowledge base. The module concludes with a focused review and assessments, aimed at reinforcing and evaluating the understanding and application of key concepts in pre-training, scaling, and fine-tuning LLMs for real-world scenarios.

LLMs for Search, Prediction, and Generation

This module on Large Language Models (LLMs) for Search, Prediction, and Generation offers a comprehensive exploration into the cutting-edge realm of language models and their transformative impact on the way we interact with digital information. Through a structured curriculum that progresses from foundational concepts, such as search query completion and word embeddings, to advanced applications, including text generation and the innovative architecture of transformers, learners will gain both theoretical knowledge and practical skills.

LangChain for LLM Application Development

In Module 3, learners will delve into the LangChain framework, designed to facilitate the development of applications powered by Large Language Models (LLMs). Through a combination of readings and instructional videos, learners will gain a detailed understanding of LangChain's foundations, its components, and its value propositions. They will also explore how to leverage LangChain to build and deploy LLM-powered applications efficiently. The module concludes with a wrap-up session and assessments to solidify learning outcomes.

Interacting with Data Using LangChain and RAG

Interacting with Data Using LangChain and RAG provides learners with a comprehensive exploration of Retrieval-Augmented Generation (RAG) models and their integration with LangChain. Through instructional videos, practical assignments, and discussions, participants gain a deep understanding of RAG fundamentals, document loading, vector stores, retrieval techniques, and building RAG models. Emphasizing both theoretical understanding and practical skills development, the module equips learners with the knowledge and tools necessary to effectively interact with data using LangChain and RAG, empowering them to build sophisticated models for tasks such as question answering and document retrieval.

Evaluating LLM Performance

This Module focuses on evaluating the performance of Large Language Models (LLMs) through various metrics and techniques. Participants will gain insights into assessing LLM performance, understanding metrics such as perplexity and BLEU score, and interpreting evaluation results. Through instructional videos, discussions, and assignments, learners will develop the skills necessary to effectively evaluate LLMs and make informed decisions about their usage in real-world applications.

Gen AI for Data Privacy and Protection

This module offers an exploration into using Generative AI for Data Privacy & Protection, designed for learners keen on advancing their expertise in this critical area. Through a curriculum that blends theoretical foundations with practical applications, participants delve into the core aspects of Generative AI for safeguarding data, and the essential considerations of ethics and compliance. This aims to equip learners with the skills to adeptly navigate the complexities of data protection, ensuring ethical integrity and regulatory adherence, thus helping them to understand the challenges of implementing cutting-edge data privacy solutions in a rapidly evolving technological landscape.

Course Wrap-up and Assessments

This module serves as the culmination of the course, where participants consolidate their learning and demonstrate their proficiency in Generative AI concepts and techniques. Participants engage in a course wrap-up session, reflecting on their learning journey and completing final assessments to evaluate their understanding of the material. The module includes a practice project to apply acquired skills in a real-world scenario and a graded assignment focusing on Gen AI architecture. Finally, participants celebrate their accomplishments with a course completion video.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops skills in using pre-trained language models and LLMs for tasks such as text generation and summarization, which can help learners automate tasks and improve content creation efficiency

Leverages the LangChain library, providing learners with hands-on experience in building LLM-based applications, enhancing their practical skills in AI development

Covers Retrieval-Augmented Generation (RAG), a technique that enriches LLM responses by incorporating relevant data retrieval, making LLM outputs more accurate and informed

Provides strategies for evaluating LLM performance, enabling learners to assess the effectiveness and accuracy of their AI solutions

Discusses the use of Generative AI for Data Privacy and Protection, addressing a highly relevant and in-demand topic in the tech industry

Emphasizes the ethical considerations related to LLM usage, promoting responsible AI development and application

Career center

Learners who complete Generative AI Architecture and Application Development will develop knowledge and skills that may be useful to these careers:

Natural Language Processing Engineer

Natural Language Processing Engineers design, develop, test, and deploy NLP systems. This course may be particularly useful in developing the skills needed to build and train advanced NLP models, which are becoming increasingly prevalent in search engines, chatbots, and other applications. The course's focus on Large Language Models (LLMs) and their applications to search, prediction, and generation would be especially relevant to this role.

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Machine Learning Engineer

Machine Learning Engineers apply their expertise in mathematics, statistics, and computer science to design, develop, test, and deploy machine learning systems. This course would provide a strong foundation for individuals interested in pursuing a career in Machine Learning Engineering, as it covers the fundamental concepts of LLMs, their training methodologies, and their applications to various tasks. The course's focus on evaluating LLM performance would also be valuable for those seeking to ensure the accuracy and reliability of their machine learning models.

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Data Scientist

Data Scientists use their expertise in statistics, mathematics, and programming to extract insights from data. This course may be particularly useful for Data Scientists interested in leveraging LLMs to enhance their data analysis capabilities. The course's focus on Retrieval-Augmented Generation (RAG) would be especially relevant for those seeking to develop data-driven applications that can effectively search, summarize, and extract insights from large volumes of text data.

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Software Engineer

Software Engineers design, develop, test, and maintain software systems. This course may be useful for Software Engineers who are interested in incorporating LLMs into their software applications. The course's focus on LangChain for LLM Application Development would be especially relevant for those seeking to develop scalable and efficient software solutions that leverage the capabilities of LLMs.

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AI Researcher

AI Researchers conduct research to advance the field of artificial intelligence. This course would provide a strong foundation for individuals interested in pursuing a career in AI Research, as it covers the fundamental concepts of LLMs, their training methodologies, and their potential applications. The course's focus on evaluating LLM performance would also be valuable for those seeking to develop robust and reliable AI systems.

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Product Manager

Product Managers are responsible for defining, developing, and launching new products. This course may be particularly useful for Product Managers who are interested in leveraging LLMs to create innovative products and services. The course's focus on the applications of LLMs to search, prediction, and generation would be especially relevant for those seeking to develop products that can effectively solve user problems and meet market needs.

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Data Analyst

Data Analysts use their expertise in statistics, mathematics, and programming to extract insights from data. This course may be particularly useful for Data Analysts who are interested in using LLMs to automate and enhance their data analysis processes. The course's focus on Retrieval-Augmented Generation (RAG) would be especially relevant for those seeking to develop data-driven applications that can effectively search, summarize, and extract insights from large volumes of text data.

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Quantitative Analyst

Quantitative Analysts use their expertise in mathematics and statistics to develop financial models. This course may be particularly useful for Quantitative Analysts who are interested in leveraging LLMs to enhance their modeling capabilities. The course's focus on LLMs for prediction would be especially relevant for those seeking to develop models that can more accurately predict future financial outcomes.

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Business Analyst

Business Analysts use their expertise in business processes and data analysis to identify and solve business problems. This course may be particularly useful for Business Analysts who are interested in leveraging LLMs to automate and enhance their business analysis processes. The course's focus on LLMs for search and prediction would be especially relevant for those seeking to develop solutions that can effectively identify trends, uncover insights, and make better decisions.

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Consultant

Consultants provide advice and guidance to clients on a variety of business issues. This course may be particularly useful for Consultants who are interested in leveraging LLMs to enhance their consulting services. The course's focus on LLMs for search, prediction, and generation would be especially relevant for those seeking to develop solutions that can effectively solve client problems and meet client needs.

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Technical Writer

Technical Writers create and maintain technical documentation. This course may be particularly useful for Technical Writers who are interested in leveraging LLMs to automate and enhance their documentation processes. The course's focus on LLMs for generation would be especially relevant for those seeking to develop solutions that can effectively create high-quality technical documentation.

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User Experience Researcher

User Experience Researchers conduct research to understand how users interact with products and services. This course may be particularly useful for User Experience Researchers who are interested in leveraging LLMs to enhance their research methods. The course's focus on LLMs for search and prediction would be especially relevant for those seeking to develop solutions that can effectively identify user needs and improve user experiences.

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Marketing Manager

Marketing Managers develop and execute marketing campaigns to promote products and services. This course may be particularly useful for Marketing Managers who are interested in leveraging LLMs to enhance their marketing efforts. The course's focus on LLMs for generation and prediction would be especially relevant for those seeking to develop solutions that can effectively create marketing content and predict customer behavior.

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Sales Manager

Sales Managers lead and manage sales teams to achieve sales targets. This course may be particularly useful for Sales Managers who are interested in leveraging LLMs to enhance their sales processes. The course's focus on LLMs for search and prediction would be especially relevant for those seeking to develop solutions that can effectively identify sales leads and predict sales outcomes.

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Customer Success Manager

Customer Success Managers ensure that customers are satisfied with products and services. This course may be particularly useful for Customer Success Managers who are interested in leveraging LLMs to enhance their customer support efforts. The course's focus on LLMs for search and generation would be especially relevant for those seeking to develop solutions that can effectively answer customer questions and resolve customer issues.

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