Prompt-Engineering

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 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 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 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 demonstrates the use of AI to generate comprehensive test cases for software modules, with examples of prompt engineering for user registration validation scenarios.