Alden Do Rosario’s Post

#AI #GenerativeAI #PromptEngineering #JSON #TaskDecomposition: "In prompt engineering, task decomposition means breaking a complex task into smaller, more manageable components. There are several reasons why this is beneficial: - Most AI tools have limitations on the amount of text they can process at once. - Large inputs can hurt accuracy, cause memory issues, or lead to the AI getting “lost” in the complexity. - Proceeding in smaller chunks allows you to course-correct issues along the way for a better end result. - Precision: Smaller prompts give you more control over the AI’s output. You can provide tailored instructions for each step, directing the final result with greater accuracy. Understanding how to break down complex tasks is essential for interacting effectively with AI tools. Even outside of prompt engineering, we do this naturally in many settings. Writing documentation often starts with a simple outline, expanding into greater detail over time. Similarly, a complex codebase is built through incremental steps, adding components over time. Learning to identify those smaller steps within a complex problem is fundamental to working with AI tools. In this tutorial, you’ll learn to create a complex tree diagram (in ASCII text) from a JSON block. Breaking this task down into smaller steps will make it easier for the AI to process and will ultimately yield a more accurate and visually appealing diagram." https://lnkd.in/dYqq99TE

A few prompts that may be of use to technical documentation pros.

I wrote a couple of posts on prompt engineering techniques related to tech comm scenarios: * Task decomposition and complex tree diagrams. This tutorial will help you understand task decomposition by guiding you through the process of creating a complex tree diagram that’s too sophisticated for an AI tool to create at once. https://lnkd.in/gg-Jq7dK * Summarizing meeting notes and amplifying visibility. This tutorial provides some prompts for summarizing meetings using AI and then broadcasting those notes out to larger groups of people. https://lnkd.in/gcXb2eDW #techcomm #technicalwriting #documentation #promptengineering #prompts #apidocs

So that LinkedIn intro course on prompt engineering was a good place to start if you know nothing, but if you really want to get into the gory details, this write-up is awesome! #AI #promptengineering

▶ LLM Triangle Principles: A Reliable Framework for AI Apps ◀ Want to build strong LLM-native apps? These principles help create reliable, high-performing AI applications by focusing on standard operation procedures and cognitive modelling, turning powerful LLMs into consistent performers. Think of your model as an inexperienced worker and teach it techniques. Cognitive modeling is the secret sauce. Identify the hidden implicit cognition jumps and create a "recipe" for success. Remember, it's not just about the model. Engineering techniques and contextual data are equally important. If you're looking to harness the full potential of LLMs for your business, send us a message! 🔗 Full article by Almog Baku: https://lnkd.in/eRvdnCEe #AI #LLM #ProductShop #Engineering #DataDriven

Want to learn more about OCR technology? Some of its challenges and how JetStream is a potential solution that goes beyond just OCR technology to achieve better results than standard OCR. https://lnkd.in/effR4Afu #documentcapture #workflowautomation #ocr #ai #icr #documentmanagement

"Abstract Generative AI has made rapid advancements in recent years, achieving unprecedented capabilities in multimodal understanding and code generation. This can enable a new paradigm of front-end development, in which multimodal LLMs might directly convert visual designs into code implementations. In this work, we formalize this as a Design2Code task and conduct comprehensive benchmarking. Specifically, we manually curate a benchmark of 484 diverse real-world webpages as test cases and develop a set of automatic evaluation metrics to assess how well current multimodal LLMs can generate the code implementations that directly render into the given reference webpages, given the screenshots as input. We also complement automatic metrics with comprehensive human evaluations. We develop a suite of multimodal prompting methods and show their effectiveness on GPT-4V and Gemini Pro Vision. We further finetune an open-source Design2Code-18B model that successfully matches the performance of Gemini Pro Vision. Both human evaluation and automatic metrics show that GPT-4V performs the best on this task compared to other models. Moreover, annotators think GPT-4V generated webpages can replace the original reference webpages in 49% of cases in terms of visual appearance and content; and perhaps surprisingly, in 64% of cases GPT-4V generated webpages are considered better than the original reference webpages. Our fine-grained break-down metrics indicate that open-source models mostly lag in recalling visual elements from the input webpages and in generating correct layout designs, while aspects like text content and coloring can be drastically improved with proper finetuning."

A recent AI research paper asks the question if front-end engineering can be automated with input to large language models that can generate the code https://lnkd.in/gzwXicAD. #frontenddevelopment #airesearch #codegeneration #artificialintelligence

The AI Form Filler addon is an experimental feature that provides an easy way for #VaadinFlow users to create forms that are filled automatically from natural language input sources using #ChatGPT technologies. See the docs 👉 https://bit.ly/47LB3zo

Creating and using effective designs and deployment in AI agents has become a pivotal focus in the world of large language models (LLMs). Here are five design patterns explored by Anthropic utilized in real-world applications.