Mayur Wadhwani’s Post

🤔 "RAG vs Fine-tuning vs LoRA" - The #1 Question I Keep Hearing from Customers After countless customer conversations, I've noticed this comes up almost every time. So I thought I'd break it down simply: 💡 RAG: Perfect when you need your AI to access fresh data in real-time. Think customer service bots that need the latest product info or legal assistants needing current case law. 🎯 Fine-tuning: Your go-to when you want the AI to truly understand your domain. It's like teaching the model to speak your industry's language fluently, though it needs more computing power. ⚡ LoRA (Low-Rank Adaptation): A smart way to fine tune AI models by adjusting just a small set of parameters instead of the entire model. Imagine tuning a few key settings rather than rebuilding the whole engine - faster, cheaper, and perfect for frequent updates! From what I've seen in the field, most customers start with RAG for quick wins, then explore the others based on their specific needs. What's been your experience? Would love to hear what's working for your team! #AI #CustomerSuccess #TechTips #GenerativeAI #MLOps

Are you among the many who see potential in AI-driven LLMs to streamline business processes, yet feel daunted by the flood of options and endless tech terms? It’s hard to keep up with all the new names and features that seem to pop up overnight. What if an AI tool dedicated to comprehensive software testing could save you both time and budget? Our latest blog post, "LLMs for Test Cases - Our Observations," is here to simplify the journey! Thanks Shahpur Khan! We break down the essentials by comparing standout models like ChatGPT and Google Gemini, with insights into their potential for our own Test Case and Test Script Generator. Dive in and let Engine Room help push your AI transformation forward. Read the full post here: https://lnkd.in/eCjXxX_2 #AITesting #LLM #EngineRoomTech #SoftwareTesting #DigitalTransformation

🤖 Exploring the Pros and Cons of Multi-Agent vs. Single-Agent AI Systems 🧠 Marco’s recent post really resonated with me. He dives into the pros and cons of multi-agent versus single-agent AI systems and does a fantastic job of weighing the complexities. While multi-agent setups like the "debating agent" pattern offer powerful capabilities, they come with challenges like managing interactions and prompt engineering. Marco’s experience with his Questionnaire Multiagent system highlights these intricacies. On the flip side, Marco showcases the strength of single-agent systems, especially with tools available in Azure OpenAI Service. These systems are simpler to manage and still incredibly powerful, often being just what you need without the added complexity. Go checkout his demo and learn more about the pros and cons of single vs multi agents systems! https://lnkd.in/gG_rEcVS #AI #MultiAgentSystems #SingleAgentSystems #AzureOpenAI

Excel dashboards eating up your time? AI can do it faster. Way faster. The solution isn't ChatGPT. I've discovered a game-changing AI tool. Here's what it does: - Analyzes your data (100% free!). - Suggests smart charts in seconds. - Builds interactive dashboards in minutes. - No formulas. No PivotTables. No design skills needed. Sounds too good to be true? There's a catch. I'll show you the pros and cons. Forget about the Excel dashboard headaches: https://lnkd.in/dsHz74U6 ❓What's your #1 dashboard frustration? Your answer might inspire the next video! #Excel #ExcelDashboard #ClaudeAI

When working with AI models, you may have the same prompt but the LLM can generate different output formats. This inconsistency can cause errors that are difficult to trace and rectify, wreaking havoc on production environments. By specifying the exact format you need – like JSON or YAML – you can avoid these pitfalls. This simple step can save you hours of debugging and ensure your AI tools work seamlessly. Just as they say, it's working smarter not harder. #llms #promptengineering #generativeai

When working with AI models, you may have the same prompt but the LLM can generate different output formats. This inconsistency can cause errors that are difficult to trace and rectify, wreaking havoc on production environments. By specifying the exact format you need – like JSON or YAML – you can avoid these pitfalls. This simple step can save you hours of debugging and ensure your AI tools work seamlessly. Just as they say, it's working smarter not harder. #llms #promptengineering #generativeai

When working with AI models, you may have the same prompt but the LLM can generate different output formats. This inconsistency can cause errors that are difficult to trace and rectify, wreaking havoc on production environments. By specifying the exact format you need – like JSON or YAML – you can avoid these pitfalls. This simple step can save you hours of debugging and ensure your AI tools work seamlessly. Just as they say, it's working smarter not harder. #llms #promptengineering #generativeai

Do you have an AI strategy? No? Don’t worry! We can help you move forward.🚀 If you are interested in having a free consultative meeting to see how your business can scale with AI - feel free to send me a DM! 📩

🌐 Are You Stuck in Yesterday’s Efficiency Loop? Think about how you spend your day at work—trapped in repetitive tasks, searching through endless docs, or stuck waiting for information. Now imagine a different kind of day—one where an AI assistant handles all that noise for you. 🚀 Welcome to a Smarter Way with QueryPal: - Instant Information: Need data fast? Ask QueryPal, and instant replies will be pulled from across your tools, such as Google Drive or Notion. - Adaptive Learning: Unlike basic bots, QueryPal understands context, evolving with every interaction to serve you better. 🔗 No Setup Required! Join our public workspace today and see the difference in real time. Forget installations; start transforming your workflow immediately. 👇 Curious? Just click the link in the comments 👇 #FutureOfWork #AI #ProductivityHacks #QueryPal

Is OpenAI's Swarm the right framework for building Agentic AI? Since its release on Friday, I've been building with Swarm and comparing it to others that I've been building on. From my perspective, #Swarm shows a lot of promise but still has a lot of areas of improvement. Let's dive in... Why is this the right approach? Agentic AI is an opportunity to build a new type of software. In this model, the user defines the input as a high level "goal" and specifies parameters of desired output. The rest should be handled by agents autonomously. Swarm (and to some extent, Microsoft's Autogen) seem to understand this better than other frameworks. The core abstractions should be Agents and Conversations (user <-> Agent and Agent <-> Agent). Some frameworks (like LlamaIndex and now CrewAI) use a flow-based approach of 2010s Airflow era! This brings back complexities like managing brittle workflows and complex interdependencies. If I need a step-by-step flow to solve a problem—with a few LLM calls at certain steps—I'd just use Airflow, Luigi, or one of the many other flow and pipeline management tools. This use case is important, but it's not inherently "agentic." Swarm is promising, but it's still a bit too simple for more complex use cases like group chat. One significant shortcoming is its lack of a robust message-handling system. Not every agent needs, or should, see every message. Allowing only relevant agents to receive specific messages reduces unnecessary complexity and ensures more efficient and secure communication between agents. Additionally, users may not want to interact directly with every agent, which Swarm doesn't currently address effectively. #AI #AgenticAI #OpenAI Swarm repo: https://lnkd.in/eufkfQkK