Generative AI

This document covers the complete AI application development journey, from ideation and model selection through building with RAG and fine-tuning to production deployment with MLOps best practices.

This document explores the multi-model approach for AI implementation covering model selection criteria, prompt engineering, continuous evaluation and collaborative team strategies for optimal AI deployment.

This document describes how to build flexible, composable chains using LangChain Expression Language (LCEL), including prompt templates, pipe operators, runnable primitives, and type coercion mechanisms.

This document describes chains in LangChain for generating responses, memory storage mechanisms, and agents for dynamic action sequencing to build sophisticated LLM applications.

This document defines LangChain and explores its core components including language models, chat models, chat messages, prompt templates, example selectors, and output parsers for building LLM applications.

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.

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, an open-source Python framework for developing LLM applications, exploring its benefits, practical uses, and integration capabilities with various data types.

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 provides a comprehensive reference guide to generative AI covering fundamental concepts, key models, applications, and best practices for implementing GenAI solutions.

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 generative AI, its evolution from discriminative AI and the foundational models that enable creative content generation across text, images, video, and code.

This document covers the wide-ranging applications of Generative AI across various domains such as content creation, drug discovery, engineering finance and healthcare.

This document provides an overview of leading tools and technologies for image generation using generative AI, including DALL-E, Stable Diffusion, StyleGAN Craiyon, Freepik, Picsart, Fotor, Deep Art Effects, DeepArt.io, Midjourney Microsoft Bing Image Creator, and Adobe Firefly.

This document provides an overview of leading tools and platforms for text generation, including LLMs like GPT and PaLM, as well as open-source and commercial solutions for creative, conversational, and code-related tasks.

This document explains hallucination in large language models, its types causes, and practical strategies to minimize fabricated or inaccurate outputs in AI-generated content.

This module explores the evolution from traditional AI to generative AI and foundation models, explaining modes of AI consumption, the importance of open knowledge, and the impact of value creation and extraction in the AI economy.

This document summarizes the generative AI module, covering key concepts including AI fundamentals, LLMs, NLP, tokens, practical applications, and tools for software development.

This document provides a comprehensive guide to tokens in generative AI covering tokenization, text processing, input limits, token pricing, and optimization strategies for AI models.