Keith Mifsud’s Post

🚀 The hidden costs of multi-agent frameworks! 💸 👇 Multi-agent frameworks can sound great, but they come with hidden challenges. Why you might want to avoid multi-agent frameworks in your AI projects: Unpredictable Model Fees: Multi-agent frameworks can increase the unpredictability of model fees and, in some cases, be expensive due to the hard-coded prompt boilerplate used on every prompt. The solution is to develop abstractions that fit most LLM APIs and SDKs. This approach can evolve over time to support new LLM features, ensuring cost efficiency and adaptability. By focusing on flexible and efficient solutions, you can create systems that are better aligned with your business needs and budget. We'll be sharing more insights on why choosing a different path for AI innovation can be beneficial, so stay tuned. #AI #llms #Software #Automation #CostManagement

With AI integration across every device, platform and toolset, AI is enabling bespoke software on demand.

𝗨𝗻𝗹𝗼𝗰𝗸 𝘁𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗚𝗲𝗻 𝗔𝗜 𝘄𝗶𝘁𝗵 𝗦𝗶𝗺𝗽𝗹𝗔𝗜 With SimplAI, you can build, orchestrate, deploy, and monitor LLM-powered applications effortlessly. Whether it's integrating global LLMs, fine-tuning with your own data, or automating workflows, SimplAI provides all the tools you need. From creating intelligent agents to ensuring the quality of LLM apps, SimplAI is your all-in-one platform for reliable, enterprise-ready AI solutions. 𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: 𝗔𝗰𝗰𝗲𝘀𝘀 𝘁𝗼 𝗚𝗹𝗼𝗯𝗮𝗹 𝗟𝗟𝗠𝘀: Choose the right model and deploy instantly with pre-built connectors. 𝗕𝗿𝗶𝗻𝗴 𝗬𝗼𝘂𝗿 𝗢𝘄𝗻 𝗗𝗮𝘁𝗮: Optimize AI performance with Retrieval-Augmented Generation and fine-tuning. 𝗟𝗟𝗠 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻: Seamlessly connect models, prompt engineering, Vector DBs, embedding, API, and data retrieval. 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗣𝗹𝗮𝘆𝗴𝗿𝗼𝘂𝗻𝗱: Test and refine prompts easily on LLMs and RAG for seamless experimentation. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗔𝗴𝗲𝗻𝘁𝘀: Create agents that leverage tools to execute complex tasks seamlessly. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀: Streamline processes with Agile AI workflows, chaining logic, data, and APIs. 𝗘𝗻𝘀𝘂𝗿𝗲 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗼𝗳 𝗟𝗟𝗠 𝗔𝗽𝗽𝘀: Evaluate prompts, models, LLM parameters, and workflows to improve feature quality. 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲 𝘄𝗶𝘁𝗵 𝗬𝗼𝘂𝗿 𝗔𝗽𝗽𝘀: Seamlessly integrate with popular apps like Slack, Confluence, Notion, and more. 💻 𝗥𝗲𝗾𝘂𝗲𝘀𝘁 𝗗𝗲𝗺𝗼: https://lnkd.in/gK7bDCNs Sandeep Dinodiya. Santhosh Kumar K. Utkarsh Mangal #SimplAI #GenAI #AIDevelopment #MachineLearning #AIInnovation #TechSolutions #Automation #AIIntegration #EnterpriseAI #LLM #ArtificialIntelligence #AIPlatform

🚀 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦 𝐘𝐨𝐮𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝐰𝐢𝐭𝐡 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬! 🚀   📊 Elevate your AI document processing to the next level with ZenStack. From data capture to email communication and document approvals, we leverage the latest advancements in LLMs and Gen AI to set a new performance standard for processing transactional documents!   🛠 Traditional document processing solutions often fall short, bogged down by manual work, slow validation, and endless email exchanges about exceptions. ZenStack changes the game with our single, cloud-native platform that automates the entire document processing lifecycle!   ✅ Automate the transactional process end-to-end. ✅ Minimize manual effort and reduce errors. ✅ Unlock strategic insights with our AI-first approach.   🚀 Say goodbye to inefficiencies and hello to a 99% automated workflow. Discover how ZenStack can revolutionize your document processing! #AI #DocumentProcessing #WorkflowAutomation #CloudNative #GenAI #LLM #BusinessTransformation #ProductEngineering #Canada #USA #UK #DigitalTransformation #Businessneeds #TrustedPartner #Aexonic #Technology #Success #ITneeds #BusinessAdvantage

Simplified Test Data Generation with Vision AI #TestData #VisionAI Streamline the testing process with advanced AI and automation. Analyze the structure of your User Interface (UI) to create tailored, comprehensive test data generation scripts Key Benefits: No Coding Required: Generate scripts with ease, even without technical expertise. Time-Saving: Automates the test data creation process, eliminating manual effort. Customizable: Easily adjust the generated code to suit specific testing scenarios.

An absolutely critical element of being able to leverage data with AI in organizations is how quickly you can make data available to AI in a way that's safe, and that means making your systems as interoperable as possible. Folks will say that if you want to integrate with a legacy system, you need to standardize your connections by adding an API. While doing a bunch of refactoring to add APIs adds billable hours and software licensing revenue to contracts, it simply isn’t necessary, or even feasible in most existing organizations. Standardization doesn't make you go faster, it actually slows you down unless you're starting with a clean slate. You don't want standards, you want modularity - reusable, recomposable building blocks in your system, like Legos. Standardization and modularity are not the same thing. Standards aren’t components, they’re rules, and they don’t work to increase interoperability and process efficiency because: a. They have to be enforced from the top down which is incompatible with the way the IT industry works (unless it benefits the providers of technology over the buyers or the competition) b. They become obsolete over time. As new standards emerge, you end up with systems that don’t talk to each other anyways. If someone tells you standards are the solution to getting systems to talk to each other, they’re simply wrong. The hidden wrong assumption is that you can update all your legacy systems to comply with the new interface standard. Not gonna happen. #AI #API #Datafabric #DevOpsForData

Absolutely agree with the emphasis on modularity over standardization. In an era where agility and adaptability are key, it's essential to focus on building systems that are not only robust but also flexible enough to evolve with technological advancements. Creating reusable components, like the Lego blocks mentioned, allows organizations to enhance their data management strategies without being bogged down by the constraints of outdated standards. This is crucial for leveraging AI effectively and safely. #DataStrategy #AgileDevelopment #TechInnovation

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An absolutely critical element of being able to leverage data with AI in organizations is how quickly you can make data available to AI in a way that's safe, and that means making your systems as interoperable as possible. Folks will say that if you want to integrate with a legacy system, you need to standardize your connections by adding an API. While doing a bunch of refactoring to add APIs adds billable hours and software licensing revenue to contracts, it simply isn’t necessary, or even feasible in most existing organizations. Standardization doesn't make you go faster, it actually slows you down unless you're starting with a clean slate. You don't want standards, you want modularity - reusable, recomposable building blocks in your system, like Legos. Standardization and modularity are not the same thing. Standards aren’t components, they’re rules, and they don’t work to increase interoperability and process efficiency because: a. They have to be enforced from the top down which is incompatible with the way the IT industry works (unless it benefits the providers of technology over the buyers or the competition) b. They become obsolete over time. As new standards emerge, you end up with systems that don’t talk to each other anyways. If someone tells you standards are the solution to getting systems to talk to each other, they’re simply wrong. The hidden wrong assumption is that you can update all your legacy systems to comply with the new interface standard. Not gonna happen. #AI #API #Datafabric #DevOpsForData

🧪 The Future of Software Testing: Embracing AI and Automation 🤖 The landscape of software testing is rapidly evolving with the advent of AI and automation. Here’s why embracing these technologies is crucial: 1. Enhanced Efficiency: AI-driven tools can identify and fix bugs faster than ever. 2. Predictive Analytics: AI can predict potential failures, allowing proactive improvements. 3. Continuous Testing: Automation enables continuous integration and delivery, ensuring high-quality releases. What AI or automation tools have you integrated into your testing process? Share your experiences and insights below! 👇 #SoftwareTesting #AI #Automation #QualityAssurance #TechInnovation