A.G. Sayyed

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.