Last year, one category AI absolutely dominated was being an extremely agreeable coworker. While this might sound nice, this can turn into a problem for founders who rely on AI as their only teammate. When your head of legal, HR, and supply operations are all AI agents, unsubstantiated flattery can create costly blind spots. That's one of the reasons OpenAI said goodbye to its " yes-man" version of ChatGPT, and why some AI-powered solo founders are training their tools to push back.
Did you know you can teach ChatGPT how to respond to certain requests? Not only can you give ChatGPT instructions, but they'll stick (mostly) for every session. This feature is called Custom Instructions. It lives in the Personalization tab of ChatGPT's settings. In a minute, I'll show you a set of really powerful directives that can help make you super productive.
Content discovery is the passive or active process of finding information on the internet using keywords. It displays previously created or indexed content, requiring users to sift through generic search results and to read through multiple long pages. Unsurprisingly, this approach often has the familiar drawbacks of content saturation and noise, leading to frustration and wasting users' time. According to a 2022 study, people who have difficulty finding content rate it as 53% less useful and accurate.
The internet is filled with supposed magic prompts that promise perfect AI outputs. Jordan Wilson sees this misconception constantly in his work, helping companies adopt AI tools. The fundamental problem is context. When someone shares a prompt that generated great results for them, they're only sharing the surface-level instructions. They're not sharing their conversation history, their accumulated context, or the back-and-forth refinement that led to that prompt actually working.
My role was straightforward: write queries (prompts and tasks) that would train AI agents to engage meaningfully with users. But as a UXer, one question immediately stood out - who are these users? Without a clear understanding of who the agent is interacting with, it's nearly impossible to create realistic queries that reflect how people engage with an agent. That's when I discovered a glitch in the task flow. There were no defined user archetypes guiding the query creation process. Team members were essentially reverse-engineering the work: you think of a task, write a query to help the agent execute it, and cross your fingers that it aligns with the needs of a hypothetical "ideal" user - one who might not even exist.
Legal training has long followed a guild system: law schools teach doctrine, then new lawyers learn to practice by working alongside experienced attorneys. This apprenticeship model may have worked well decades ago, but it's increasingly problematic today. And it particularly doesn't work well when it comes to GenAI since all too often younger lawyers know more about the tools than more experienced lawyers.
I've learned which skill sets software engineers need to land a job offer in the AI era. Tech companies agree that AI makes engineers more productive, so engineers are expected to use it to build things more quickly and reliably. I personally make heavy use of AI to help me with boilerplate stuff so that I can concentrate on the hard stuff, like system design and complex business logic.
Knowing how to query ChatGPT, Gemini or Claude doesn't make you an expert on genAI and AI tools. There's a lot more to learn about in this fast-evolving area of tech. Even as thousands of workers lose their jobs to AI, the number of job openings seeking "AI skills" continues to grow. Mentions of AI skills in job postings rose 5% year over year, according to data released in December by staffing firm Manpower Group.
A typical AI-powered tool leverages the "knowledge" gained from previously processed image libraries. The more vivid these databases and the more additional information they include - like detailed descriptions of objects in pictures or other tags that may simplify visual object recognition- the better the final result a tool delivers. The origin of processed databases and the legal safety of generated images are other critical issues regarding AI-powered tool usage.
The rise of GenAI tools has introduced a new paradigm for design work - rather than crafting interfaces by directly manipulating elements in design tools, designers can prompt AI-prototyping tools to generate designs. When you're working with AI, the output quality will be largely dependent on the specificity of the prompt. This article discusses common issues with vague text prompts and offers recommendations on how to achieve better results from AI-prototyping tools without undertaking the bulk of the design work yourself.
Bola Rotibi, chief of enterprise research at CCS Insight, said professionals should take practical steps to protect themselves. "Get fluent in prompts, verification, and basic data ethics and rules, such as GDPR and audit trails," she told ZDNET. "Shift from coordination to owning an end-to-end outcome. Define the task, select the tool, verify the result, and sign off. That human failsafe of design, integration, and governance is where resilience lives."
Agency teams are under pressure to move faster, deliver sharper work and manage a growing mix of AI-enabled tasks-all without losing strategic rigor or creative edge. As GPT models become more capable, they're shifting from simple drafting tools to support partners that can strengthen research, planning and production workflows. Yet the real advantage comes not from the technology itself, but from how well professionals guide, structure and refine its output.
I recently realized something while building Podscan, my podcast database system that does a lot of background data extraction and analysis for my users. I've stumbled upon a couple of AI integration best practices that a lot of people might not be fully aware of. So today, I want to dive into the concepts I found not just useful, but essential for maintaining and operating mission-critical data handling with LLMs and AI platforms.
The venture capitalist said on an episode of the "a16z Podcast" published Tuesday that AI tools can act as the "world's best coach, mentor, therapist, advisor, board member" for anyone who asks the right kind of questions. AI is probably "the most democratic" technology of all time, said the cofounder of VC firm Andreessen Horowitz. "The very best AI in the world is fully available on the apps that anybody can download."
AI slop is here, it's ubiquitous, it's being used by the US president, Donald Trump, and now, it's the word of the year. The Macquarie Dictionary dubbed the term the epitome of 2025 linguistics, with a committee of word experts saying the outcome embodies the word of the year's general theme of reflecting a major aspect of society or societal change throughout the year.
direct, hands-on experience with Claude and help them build the confidence and skills to collaborate effectively with AI
One prompt = one image. Don't ask ChatGPT to generate a few different images in a single prompt because you will likely get a messy output. If you need a few images, simply submit a few prompts. Aspect ratio. If you generate illustrations or background patterns, specify the aspect ratio that aligns with your goal. For example, if you want to generate a hero image for the web, use 16:9, but if you want to have imagery for mobile, use a 1:1 ratio.
- style raw. I use it to achieve sharper, cleaner UI details. It helps avoid overly artistic blur - ar 16:9. As you probably guessed, this is the aspect ratio. I typically use ar 16:9 for web and ar 4:5 for mobile. - v 7. Midjourney version that will be used to generate visuals. I typically use the latest version v7. But I've noticed a very interesting thing: v7 tends to generate pseudo 3d layouts, while v6 typically generates flat layouts.
Crafting a perfect prompt for AI chatbots is often a challenge - so much so that startups are creating roles for prompt engineers. Consumer-facing AI apps are increasingly adding features like suggestion buttons or autogenerated recommendations to nudge customers to use the chatbot more frequently and show them about what the app can do. Hero, a productivity startup founded by former Meta employees, announced a new autocompletion SDK today that will fill in prompts for you based on context.
