This document introduces generative AI, its evolution from discriminative AI and the foundational models that enable creative content generation across text, images, video, and code.
This document explores foundation models and large language models, covering their training methodology, advantages in performance and productivity, as well as challenges related to compute costs and trustworthiness in enterprise applications.
This document introduces natural language processing, explaining how computers translate between unstructured human language and structured data through techniques like tokenization, stemming, lemmatization, part of speech tagging and named entity recognition.
This document provides a comprehensive reference guide to generative AI covering fundamental concepts, key models, applications, and best practices for implementing GenAI solutions.
This document introduces in-context learning and prompt engineering explaining how LLMs can learn new tasks from examples provided in prompts without additional training, along with techniques for crafting effective prompts to guide AI systems.
This document introduces LangChain, an open-source Python framework for developing LLM applications, exploring its benefits, practical uses, and integration capabilities with various data types.
This document explores advanced prompt engineering methods including zero-shot, few-shot, chain-of-thought, and self-consistency techniques, along with practical tools and applications for effective LLM interactions.
This document introduces LangChain Expression Language (LCEL), covering how to build flexible chains using the pipe operator, structure prompts with templates, and develop reusable patterns for AI applications.