#prompt-engineering

#prompt-engineering

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.

Nano Banana Pro (Gemini 3 image model) is great for UI design tasks thanks to its impressive text accuracy, strong understanding of layout, and support for crisp, high-resolution 4K outputs. I've shown how this tool can streamline common UI design: In this article, I want to show you 5 less conventional (but very cool) ways of using this tool. 1. Generate bad design first to reveal good solutions

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.

How to Build and Deploy Agentic Systems Paige Bailey, DevRel Lead of GenAI at Google Agentic systems are emerging as a key paradigm in how we design intelligent applications that can reason, act, and adapt in dynamic environments. In this interactive workshop, Paige Bailey will introduce the foundational concepts behind agentic systems and demonstrate how to build them using CrewAI and Google's Gemini APIs in Google Colab.

AI has reimagined the whole advertising ecosystem, a big part of that being creative. As AI develops, more options present themselves: we've now reached a point where it's possible to let AI execute the majority of the processes involved in carrying out an ad campaign, with just minimal human intervention here and there. And the requirement for any human interaction is diminishing.

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."