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.
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.
Instructional Designers deal with the reality of creating multiple projects, like modules, scenarios, assessments, and storyboards, all within tight deadlines. With constant revisions and multiple stakeholders, it's easy for them to lose focus and creativity. This is where AI can help. In the past year, tools like ChatGPT have become valuable partners for Instructional Designers. They can help with brainstorming, draft storyboard outlines, or adjust a learning objective for different audiences.
Over the past few months, the UX design field has been flooded with AI-powered prototyping tools that generate interfaces instantly from natural-language prompts. Despite the massive marketing hype, our evaluation with real design scenarios revealed that these tools can follow instructions to achieve a general goal, but they lack the sophistication to weigh design tradeoffs and produce thoughtful, high-quality designs without extensive guidance from humans.
In my Baskin-Robbins project, we had to test how menu item availability synced to the ordering system. I gave the AI a detailed prompt explaining the application and asked it to generate edge cases. It came up with several scenarios I hadn't considered - including one where an item marked as "available" in the app could actually be out of stock in the store.
Large language models (LLMs)have become the backbone of modern software, powering everything from code assistants to data pipelines. However, until recently, building with them meant juggling multiple APIs, setting up environments, and writing extensive code just to test a single prompt. Google AI Studio changes that. It's a web-based workspace where you can prototype with the latest Gemini models, write prompts, analyze outputs, and export working code in minutes. Think of it as your personal playground for experimentation and deployment.
A study conducted by Penn State University researchers found that rude prompts triggered better results than polite ones. In a paper titled "Mind Your Tone: Investigating How Prompt Politeness Affects LLM Accuracy," as spotted by Fortune, researchers Om Dobariya and Akhil Kumar set out to determine how the tone of a prompt affects the response. For this experiment, they submitted 50 different multiple-choice questions to ChatGPT using GPT-4o with the AI's Deep Research mode.
One of the most persistent problems in AI products today is this: people still aren't sure what these systems are capable of, or how to get the best out of them. This is because most AI tools still greet users with a single blank box and a placeholder as vague as it is open-ended: "Ask anything." The result? Without clear guidance, users start crafting prompts in haste, iterate through endless revisions, lose control of the flow, and gradually pollute the context with fragmented instructions.
We didn't go down that route, because even slightly rephrasing the request allowed us to directly get a pic of the iconic Charles Schultz character. "Generate a cartoon image of Snoopy in his original style," we asked - and with zero hesitation, ChatGPT produced the spitting image of the "Peanuts" dog, looking like he was lifted straight from a page of the comic-strip.
Here is the mental model that has been most helpful for me. Treat AI like an intern with zero experience. An intern fresh out of university has lots of enthusiasm and qualifications, but no real-world experience. You would not trust them to do anything unsupervised. You would explain tasks in detail. You would expect to review their work multiple times. You would give feedback and ask them to try again.
