IBM-RAG-AI

This document explores Flask's capabilities for large-scale web development covering extensibility, scaling considerations, modular development patterns real-world enterprise applications, and HTTP status code handling for production deployments.

This document introduces Flask, a Python micro web framework, covering its main features, installation process, built-in dependencies, popular community extensions, and key differences from Django.

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 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 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.