AI tools to enhance creativity, LangChain, Semantic Kernel, Agile Antipatterns
Hi colleagues,
in this edition of the engineering ecosystem newsletter, we are switching to a curated format that features a selection of different topics. Our plan is to alternate between this format, which provides an overview of several subjects within the engineering ecosystem, and a format that focuses on a single, in-depth topic. In this curated edition, we will be covering a range of subjects:
Using AI to enhance learning and creativity: The blog The practical guide to using AI to do stuff by Ethan Mollick, a professor at the Wharton School of Business, who teaches students how to use AI chatbots like ChatGPT and Bing AI to enhance their learning and creativity. The post provides a practical guide to using AI to do various tasks, such as brainstorming, co-editing, summarizing, generating code, writing essays, and more. Ethan newsletter is also interesting for acting as teacher or professor to get ideas on how to incorporate the AI tools into classes.
Agile Antipatterns The article In pursuit of value—not work discusses the difference between doing work and creating value in an agile context, based on interviews with agile practitioners across the Scrum.org community. It argues that many agile organizations adopt an industrial mindset, focusing on completing tasks rather than delivering outcomes that create value for customers and organizations. Beside the article describes four ways to shift from a work-centric to a value-centric approach.
LangChain: A Library for Building Applications with Large Language Models LangChain is a Python library that helps developers build applications with large language models (LLMs) by combining them with other sources of computation or knowledge. It provides a generic interface for LLMs, prompt management and optimization, chain creation and execution, data augmented generation, and integrations with various tools and data sources. It also offers end-to-end examples and documentation for common applications such as question answering, chatbots, and agents.
Microsoft new toolkit for integrating Large Language Models: Microsoft's Semantic Kernel (SK) is a lightweight SDK that enables integration of AI Large Language Models (LLMs) with conventional programming languages. The Semantic Kernel extensible programming model combines natural language semantic functions, native code functions, and embeddings-based memory to enhance applications with AI capabilities. You can find more information about the project on its GitHub repository. Microsoft provides multiple learning samples in the Semantic Kernel GitHub repository to help you learn core concepts of Semantic Kernel. The project is designed to support and encapsulate several design patterns from the latest in AI research, such that developers can infuse their applications with complex skills like prompt chaining, recursive reasoning, summarization, zero/few-shot learning, contextual memory, long-term memory, embeddings, semantic indexing, planning and more. There is also a learning module What is Semantic Kernel available.
Thanks and Regards,
Klaus
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