DSPy Meets MCP From Brittle Prompts to Bulletproof

DSPy Meets MCP: From Brittle Prompts to Bulletproof AI Tools

You’ve carefully crafted the perfect prompt for your AI tool integration. It works beautifully—until it doesn’t. A slight change in input format or a different model version causes your carefully engineered prompt to fail. Sound familiar? This brittleness plagues traditional AI tool integration, where success depends on manually crafted prompts that break under real-world conditions.

Enter DSPy and the Model Context Protocol (MCP)—a powerful combination that transforms fragile prompt engineering into robust, self-optimizing AI systems.

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Understanding LLMs and Using Chain of Thought

***This article was originally published on Understanding LLM and using Chain of Thoughts on July 24, 2023 by Rick Hightower.***

Understanding LLMs and Using Chain of Thought

Author: Rick Hightower

We will look at a real-world use case that most developers and tech managers should understand. We will give ChatGPT a Java method and ask it to create a Mermaid sequence diagram.

Chain of Thought (CoT) prompting is a technique that improves the performance of Large Language Models (LLMs) on reasoning tasks. It uses few-shot learning. According to Toward Data Science, CoT helps LLMs handle complex tasks like common sense reasoning and arithmetic. It does this by breaking down multi-step requests into smaller steps. This creates a way to see and understand the process. It makes both the input and output easier to manage and tweak.

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AI-Powered Knowledge Base for Product Managers

AI-Powered Knowledge Base for Product Managers

Author: Rick Hightower

This article originally appeared on August 7, 2023 on LinkedIn.

Building an AI-powered Knowledge Base for Product Managers

An IBM study shows product managers are early adopters of generative AI. They rank in the top-ten professions that use AI. The report states that 21% of product managers use AI daily. Product Managers are leading the AI charge.

As AI’s role in product management increases, product managers must learn how to use AI to stay competitive. Product managers using AI differ from their traditional counterparts by applying their technical expertise to harness AI’s potential in enhancing product management processes.

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PrivateGPT and LlamaIndex Revolutionizing AI Proje

In the dynamic world of AI development, PrivateGPT has emerged as a groundbreaking tool, offering a robust, private AI solution. Recently, I’ve integrated PrivateGPT into a project, enhancing it with custom jobs using LlamaIndex—a shortcut for implementing Retrieval Augmented Generation (RAG) support. PrivateGPT is remarkably easy to modify and extend. LlamaIndex serves as a shortcut for using LangChain to build RAG support, while PrivateGPT has been our go-to for building a backend tool for our GenAI needs. It allows us to effortlessly switch between vector stores and LLMs. This experience has been nothing short of transformative, highlighting the versatility and adaptability of PrivateGPT and LlamaIndex in real-world applications.

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