Ercin Dedeoglu’s Post

Here's the scoop on what to ask your platform provider about their AI. Question 2: How reliable are your AI results? How often does the AI hallucinate? The whole point of using AI tools is to streamline tasks and make your workweek more efficient. But if the results fall short or you spend too much time fine-tuning the AI, it defeats the purpose. Ask your provider if you can test their AI with a practical example to gauge its effectiveness in improving your daily workflow. Ask questions about how their technology minimizes hallucinations. Is the provider using guardrails to prohibit specific language or topics that can be problematic? Are they using consensus algorithms and/or “RAG” (retrieval augmented generation)? If they aren't willing to do that.. you might want to keep looking....

Explainable AI (XAI) refers to an AI system's ability to explain its decision-making process in a way that humans can understand. In answer engines, XAI helps users grasp how the system reached a particular answer. So, why is explainable AI important for answer engines? Let’s take a closer look. #ExplainableAI #XAI #ArtificialIntelligence #AnswerEngines #MachineLearning #Splore

Expectations vs. reality: A real-world check on #generativeAI As Mary Branscombe writes in CIO Online: "Gen AI takes us from single-use models of machine learning (ML) to AI tools that promise to be a platform with uses in many areas, but you still need to validate they’re appropriate for the problems you want solved, and that your users know how to use gen AI effectively." https://lnkd.in/gpYEBvds Our recent experience also shows an increasing interest in - and need for - companies to invest in appropriate #AI usage policies, frameworks and architecture - as well as training people up to change the way they work and even shift into new occupations.

Generative AI is a hot topic across industries, but few organizations know its abilities—and its limitations. In this #TEKsystems / #OneNorth article, we explain why you’re asking the wrong questions about AI strategy and explore what AI can and can’t do. Check out the article here: https://lnkd.in/dD3aDZth

By now, we all know that AI is valuable, but how do we implement it successfully? With so many decisions to make, it's easy to feel overwhelmed. Decision-makers often lack direction and understanding when it comes to deploying and leveraging AI and ML. A recent report reveals that there are three specific challenges businesses must overcome to get the most from these technologies. 1. AI must be built on a high-quality data foundation.  2. Clear use cases and a strategy to move quickly must be established.  3. The skills gap must be overcome. In this article you will see how each of these challenges presents remarkable opportunity for growth. #AI #MachineLearning #DataFoundation #ResponsibleAI https://lnkd.in/eWMn5f-j

With inclusion of AI in our work, there is no need to write a solution from the scratch as AI would give us a base solution and from there we need to extend it. AI will not make us lose our jobs. But if we don't know how to use AI in our work, we certainly will be lagging behind.

Prompt engineering crafts instructions for AI models, shaping their response to your request. Imagine it as fine-tuning a question - the better you ask, the better the AI understands. Clear and detailed prompts lead to accurate and relevant outputs from the AI. It's like the key unlocking an AI's potential, guiding it towards the answer you seek. Mastering prompts refines your interaction with AI, turning it into a powerful tool. Thank you #AWSSkillBuilder to teach about prompt Engineering and to upskill the way on how to create an prompt.

What is the CRITTER Framework? - Context - Role - Input - Task - Tone - Evaluation example - Response format A systematic approach to creating AI prompts that are clear, comprehensive, and capable of generating high-quality outputs. #ai #prompt #engineering https://lnkd.in/ewFvhYbJ

prompting and AI Prompting is the process of providing AI with a context or a cue that guides its responses or actions. It involves more than asking questions; it’s about crafting inputs that are designed to deliver the most informative, relevant, and desired output from an AI system.

What is the CRITTER Framework? - Context - Role - Input - Task - Tone - Evaluation example - Response A systematic approach to creating AI prompts that are clear, comprehensive, and capable of generating high-quality outputs. #ai #prompt #engineering https://lnkd.in/d5ZryT7U