Claude, Perplexity, Microsoft Copilot, Gemini, ChatGPT, You... or... WHAT?
Claude, Perplexity, Microsfot Copilot, Gemini, ChatGPT, You.com?
What Generative AI model and tool should you choose?
I can understand how overwhelming this may be. In fact, it is for me, too. There more I learn about Generative AI the more tools I discover and explore.
The market is flooded with tools that do music, videos, images, content, dating, programming, narration and everything in between. And at least when it comes to choosing an Generative AI assistant, such as Claude or Copilot or ChatGPT, things can get a little bit confusing.
You can avoid reading the rest of this article by just doing this: experiment. Create a free account in all the models and tools, experiment with them and test the quality of the output, and choose what works for you. However, if you want to save some time, continue reading for some tips.
Please note that I created these tips thinking of you as an individual person, not a organization. Some of the insights shared in this post may apply for an organization looking to implement AI solutions, but they are more valuable for you as an individual person.
Dimensions and Tests
Here are the top 10 categories I would recommend considering when assessing which AI model or tool to use, especially in the context of HR. In addition, I included three ways in which you can test the models or tools in each the dimensions in order to form a thorough and informed perspective about which tool can help you best.
Conversational Abilities: How natural, contextual, and human-like the AI's responses feel in a back-and-forth dialogue.
Knowledge Breadth: The range of topics the AI seems knowledgeable about beyond just factual recall.
Creative Proficiency: The AI's aptitude for open-ended tasks requiring imagination, such as ideation, storytelling, or humor.
Analytical Skills: The depth of analytical reasoning and problem-solving capabilities the AI exhibits.
Emotional Intelligence: The AI's ability to perceive, understand, and respond appropriately to emotional tones and social cues.
Personalization: How well the AI can tailor its personality, communication style, and responses to the individual's preferences.
Trustworthiness: The sense of reliability, honesty, and ethical grounding the AI projects in its outputs.
Learning Pace: How quickly the AI seems to accumulate new knowledge and adapt to the user's inputs over time.
User-Friendliness: The intuitiveness and accessibility of the interface for communicating with the AI.
Cost and Access: The financial and technical barriers to using the AI for an individual's needs.
Limitations and Risks
I “trust” these tools, but not blindly. I don’t share personal or corporate data. In addition, if you can, create your account using your personal email (nothing wrong with using a corporate email, but just in case).
AI models and tools are amazing and powerful. But as “smart” as they may sound, they lack true comprehension and deeper human reasoning abilities. They excel at pattern recognition and generating coherent responses based on their training data, but cannot genuinely understand context the way humans do. This lack of deeper understanding can sometimes lead to nonsensical or biased outputs, especially on complex topics the AI hasn't adequately learned. That’s why you have to be patient, design great frameworks for prompting and, more importantly, add the human touch. Remember: “AI won’t replace your job, but a human using AI will”.
AI is only concerned with outputs, and you should be concerned with outcomes. The output is the end-result of your input and AI help. But, the outcome, the impact you are trying to achieve, is optimized by taking AI’s output and adding the human touch and element to it. That’s the kind of collaboration you should keep in mind.
5 Ways to Create a Testing Case
Finally, here are some ideas to truly assess an AI model's capabilities across conversational abilities, knowledge breadth, creativity, analytical skills, and more, it's important to test them with complex, real, kind-of-human, scenarios that mimic real-world HR situations.
Use “Real” Employee Data
Create “real”, but anonymized employee date. For example, feed the AI authentic details about job roles, performance, grievances etc. to evaluate its context comprehension.
Craft Multi-Layered Narratives
Don't just use simple prompts - build intricate narratives that interweave different HR aspects like managerial relations, policy conflicts, cultural tensions etc. Introduce emotional components, ethical dilemmas, and shifting contexts to test the AI's ability to navigate complexity.
Incorporate Multimedia
Go beyond just text by including audio clips, images, videos, data visualizations and more in your testing cases. See how well the AI can perceive and integrate information across different mediums. Not all models allow for multimedia inputs, but test the ones that do.
Crowdsource Edge Cases
Crowdsource unique challenges from a diverse group of HR professionals, managers, and employees across your organization or peers in your network. Their personal experiences can uncover edge cases that stress-test the AI's reasoning abilities. Do the exercise together with them. Try to fry AI’s brain to see how far you can stretch its capabilities, and which of the available ones is… the one (for you).
Iterate and Evolve
Don't just evaluate once - create an iterative process where you continuously evolve your testing cases based on the AI's performance. Identify areas where it struggles and introduce new complexities to push the boundaries of its capabilities.
By Enrique Rubio (he/him)
(Note: I write fast, speak fast and read fast. Sometimes - often times - I don't see the typos! Please let me know if there are typos or grammatical misconstructions in my write ups. It happened to Shakespeare, it certainly can happen to me. Do so with kindness, compassion and grace, though. I appreciate it!)
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Great Summary on why and how to use AI
Well HighlightEd Hacking HR
Technologist, Coach, Mentor, People Manager
5 个月Great take-away Remember: “AI won’t replace your job, but a human using AI will”. AI is only concerned with outputs, and you should be concerned with outcomes. The output is the end-result of your input and AI help. But, the outcome, the impact you are trying to achieve, is optimized by taking AI’s output and adding the human touch and element to it. That’s the kind of collaboration you should keep in mind.
Doctor of Computer Engineering | Automotive Cybersecurity | Artificial Intelligence | Human Resources Management
5 个月To enhance the evaluation of generative AI tools for your HR purposes further, you could consider also evaluating how easily each AI tool can integrate with your existing HR systems, such as HRIS (Human Resource Information System), LMS (Learning Management System), or ATS (Applicant Tracking System).