Retool’s Post

Companies can now deploy their own ChatGPT-like experiences with this template. But unlike ChatGPT: ◆ It can generate and stream UI, not just text & markdown ◆ It's fully open source ◆ It can point to any AI model (open like Mistral or SaaS like OpenAI) ◆ It leverages your existing React component investments Check out the demo and deploy it: https://chat.vercel.ai

Next.js AI Chatbot 2.0 ◆ AI SDK 3.0 with React Server Components ◆ Generative UI support ◆ Updated Shadcn UI and Next.js ◆ Simplified deployment https://lnkd.in/gEunWYV6

While a RAG-based architecture won't be the primary focus of our upcoming AI assistant for SaaS apps, it will have the ability to answer questions using contextual information from key documents. While I was building Locusive's free chatbot, I implemented a straightforward RAG architecture that included your standard embedding -> indexing -> query pipeline using Pinecone. However, I learned a few things as I was doing so, and when we implement RAG for the Concierge, I'd like to optimize a few things, namely: - Creating a hybrid search function to account for low-information keyword queries - Adding metadata into the body of the text that's embedded - Adding the ability to perform operations on a single document, vs. only using chunks from multiple documents Here are a couple of good articles I'm using as a reference: - https://lnkd.in/eiPh8e4S - https://lnkd.in/e6nHDhhq

💰 Reduce the cost of Keeping The Lights On by 30-60%?! Typical platform teams spend most of their early engineering budget "Keeping The Lights On" with operational tasks that can quickly be offloaded to AI powered Digital Assistants! Context-aware Digital Assistants respond to stackoverflow-style queries in the RunWhen web UI, Slack, IDPs and other developer tools to suggest scripts to run. Find out how your team can save up to 60% of KTLO costs at runwhen.com/cost-saving #costreduction #keepthelightson #KTLO #RunWhen #WhatDoIRunWhen #AI #GenAI #DigitalAssistant #Kubernetes #k8s

I could use some help here: What is the best platform or software to store/organize/create prompts that integrates user feedback in a nice UI so that you can refine/test/iterate on GPT bots?

What are the key parts of a GenAI application? (looks simple, complex inside, obviously) Here's a breakdown of GenAI application key construction blocks: ☑️ User Interface (Frontend) ↳ Interact with AI through chatbots or web apps. ↳ Offers a smooth experience, but the real work happens behind the scenes. ☑️ Orchestration Layer ↳ Acts like a conductor. ↳ Ensures user requests go to the right AI services efficiently. ☑️ Backend ↳ Where the data is stored. ↳ Uses databases and caching systems like Redis and SQLite. ↳ Keeps AI responsive. ☑️ APIs & Hosting ↳ Connects developers to AI models. ↳ Open-source and proprietary APIs for integrating AI into apps. ☑️ Developer Tools ↳ Essential toolbox for creating prompts, embedding AI models, performing validations, and adding functionality. ↳ Includes developer plugins. ☑️ Infrastructure ↳ The invisible backbone. ↳ Includes hardware and cloud services with powerful GPUs. ↳ Provides computing power for running AI models. Couple of takeaways: - The frontend is designed to be simple. - The backend is a complex mix of services and infrastructure. - Each part is crucial for delivering a seamless AI experience. And of course this is an extremely oversimplified overview. Have I missed anything big here? Follow me, Evgeny Krapivin, for similar insights, I'm gonna publish a lot of relevant content in the upcoming weeks!

Spring AI + Vaadin is a powerful combo. Here's a minimal chatbot with a web UI in less than 20 lines of code 👉 https://lnkd.in/gXGH3Xkd

With 10-80-10 (10% human input in the frontend, 80% AI automation, 10% human approval at the backend), brands can harness of power of AI to scale content while remaining in control and ensuring brand safety.

🚀 Lazy AI now has Lightning-Fast Deployment! ⚡️ Say goodbye to long waits and hello to instant gratification with our latest feature! Fed up with the tedious wait for your app to deploy during testing? We’ve got you covered. 😉 With our groundbreaking update, the first load installs all dependencies, and every subsequent "run" deploys your app at lightning speed. ⚡️ "But hey, isn’t 2-5 seconds still a bit slow?" Well, the last time we checked, lightning wasn’t deploying software either. 😉 Lazy AI 1 - 0 Lightning Ready to experience the speed? Give it a try now!

The AI landscape on the frontend is changing. And a new paradigm called Generative UI is evolving ! Right now you can build interactive apps with LLM as the backend. In this video I discuss what's new on Vercel's AI SDK 3.0 update and how it could potentially change how we build chat-based applications. https://lnkd.in/d8pKAu9J