Applied Generative AI with Langchain and RAG by PwC Academy

LangChain is a framework that helps developers create applications using large language models (LLMs). It enables the ability to connect language models with other data sources, APIs, or workflows defined by the user to create complex, dynamic applications.

Advanced artificial intelligence models known as Large Language Models (LLMs) have been created to comprehend, produce, and control human language. They receive extensive training on large volumes of text data and apply deep learning methods, specifically transformer architectures, to understand the statistical connections among words and phrases.

LLMs have the ability to complete various tasks such as text creation, translation, summarization, and answering questions, frequently providing responses that resemble those of humans.

LangChain extends the language models with capabilities to interact with all different types of data sources, APIs, and tools. That would mean that it is capable of engaging in more complex activities than just basic text generation, such as querying databases, using external knowledge bases, and sequential reasoning.

The core features of LangChain include a modular chain-based design, interface capabilities with a wide range of data sources, dynamic memory management for conversations, and workflow management and customization tools for integrating language models. It is also an agent-suitable interface that lets the models execute multi-step tasks.

Retrieval-Augmented Generation (RAG) combines retrieval-based methods with generative models, creating a hybrid approach in natural language processing. It initially obtains pertinent documents from a knowledge base by employing search algorithms that consider a specified input query. Next, a transformer-like generative model produces logical answers based on the gathered data. This approach improves the precision and pertinence of results by guaranteeing that the generative model has access to current data. RAG is especially beneficial in applications like chatbots, question-answering systems, and content generation, offering responses that are more contextually suitable and dependable.

Generative AI and LLM engineers are tasked with creating, training, and improving generative AI models. They gather and preprocess data, adjust models for particular needs, and incorporate them into live systems. Their responsibilities include improving performance, guaranteeing expandability, and tackling ethical issues such as reducing bias.

They work with diverse teams, keep abreast of AI developments, and guarantee responsible AI implementation while providing precise and effective AI solutions for different sectors.

The majority of companies rely on software engineering to create and maintain digital products and services.

This Applied Generative AI with Langchain and RAG Course by PwC Academy is specifically designed for:

A fundamental understanding of machine learning, deep learning, and AI principles, paired with skill in Python coding, especially with AI and ML libraries. It is advisable to be acquainted with large language models. Having knowledge in working with data, such as preprocessing and manipulation, is advantageous.

The following specifications are the recommended system requirements for this Applied Generative AI with Langchain and RAG Course by PwC Academy:

Detailed step-by-step installation guides are available on the LMS. If you have any doubts, the 24/7 support team will promptly assist you.