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CNET
2 days ago
- Entertainment
- CNET
I Review AI Image Generators. This Is How I Write My Prompts to Get the Best Results
In the messy world of AI image generators, there are a lot of things that can go wrong when you're trying to get the image you see in your head to appear on your screen. I've spent the past year testing and reviewing different AI image generators, and I've generated hundreds of images across services like Midjourney and Dall-E. But they haven't all been winners. A bunch of them have been downright horrifying. But it taught me that the best way to avoid creating a wonky AI image is using a good prompt. Prompt engineering, as experts call it, is knowing what words to use to get AI products to do what you want. For AI images, that means creating a holistic description of what you want, beyond just the characters and setting. I've written a lot of prompts through my testing, and I've learned that no matter what service you use, there are essential elements you need in every prompt for the best results. This is especially important if the generator you're using doesn't have a lot of editing tools, like the ability to upload reference images or fix weird hallucinations. Writing a good AI image prompt is very easy to learn. These are my best expert tips for crafting the right prompt, including some common phrases to use and common mistakes to avoid. Start with these three elements When you first write your prompt, you might feel overwhelmed or like you're not sure where to start. I've been there, and the best place to begin is with the essentials. These are the three necessary elements every prompt needs. Once you have something for each of these, you can build it out from there. Characters and elements in the scene Setting or where it takes place Dimensions, like portrait, landscape or a specific ratio (3:2, 16:9, etc) You might be tempted to add some exclusionary characteristics in your prompt, or things that you do not want in your image. I would caution against it. Even the most prompt-adherent generator is likely to ignore these, or worse, misread the prompt and include something you specifically asked it not to. If you want to eliminate an element from one image, it's usually easier to do that in the editing stage rather than in the original prompt. Specify the style and color palette you want Beyond the "who, what and where" in your basic prompt, you'll want to guide the generator toward a specific style. Here are some of the most popular styles of AI images. Photorealistic: As close to real life as possible. AI image generators aren't great at this, but it's worth trying. Stock photography: Like real photos, but shinier and brighter. Product features: Emphasizes individual elements over the background or scene. Cartoon: Fun, bright and usually less detailed. Illustration: Similar to paintings, pencil sketches. Gaming/Game UI: More advanced than cartoon, sometimes anime-like. Include specific colors you want, too. If you're not picky about the exact shades you want, you can still lead the generator down the right road by specifying if you want warm or cool tones. This Canva image keeps the magic alive with a cartoonish warm-toned image. Katelyn Chedraoui/Canva Magic Media AI You'll want different styles for different projects. Photorealistic AI images are likely to be better suited for professional environments than cartoon-style images, but they might not be right for a creative mock-up. Illustrations might be best for more detail-oriented, creative projects, like building out brainstorming ideas, and gaming is good for first iterations of new characters and worlds. Describe the aesthetic, vibe and emotion Take your prompt a step further and include a description of the overall aesthetic or vibe. This can help elevate your images and reach that extra layer of detail. These details are a jumping-off point to get you in the ballpark of what you want without overwhelming the generator with a novel-length prompt. Here are some common options to include in your prompt. Abstract Anime Medieval Retro Psychedelic Glow, neon Geometric Painting, brushstroke, oil painting Comic Noir Vintage Impressionist Simple, minimalistic Fantasy, sci-fi High tech Surrealist If none of these aesthetics feel right, try picking the closest one and building from there. Include textures, the time period and landmarks. If you care less about the specific style but want to ensure a specific emotional response, try describing that. Often describing the emotional temperature of a scene can jump-start the generator toward a specific kind of visual look. For example, happy scenes tend to have bright colors and a warm feel, no matter if they're photorealistic or illustrations. Stressful scenes might have more detail, cool tones and a foreboding feeling that the generator might show you fits better with a fantasy or nonrealistic aesthetic. Leonardo might not understand "cottage core coastal grandma," but it does understand the rustic feel with blues and warm light. Katelyn Chedraoui/Leonardo AI You can try using more specific or pop culture aesthetics, but there's no guarantee the generator will understand and adhere to them. For example, you might want to consider translating "cottage core coastal grandmother" to "vintage style with a light, breezy, feel using pastel blues and neutral tones." It gets at the same idea with more specific instructions. My AI images still aren't right. What now? Even with a well-written prompt, AI image generators aren't perfect and you'll get some duds. The tech behind the text-to-image generators is advancing, but it's still very much in progress. Tweaking your prompt is the fastest way to troubleshoot big problems. But if issues persist, try narrowing down what exactly is wrong with the images and tracing the problem back to where it may be coming from. For example, if your images aren't professional-looking enough to present, it could be because the style or aesthetic included in your prompt isn't right. Even making smaller changes to your presets, like the image dimensions, can make a big difference in the end results. Midjourney took the "stressful" emotion too far in this image and lost the photorealistic style I wanted. Katelyn Chedraoui/Midjourney AI Many AI image services offer post-generation editing tools that can help you fix smaller errors. Services more geared toward professional creators like Adobe Firefly have extensive tools. More beginner-friendly programs run the gambit, with Leonardo having the most, then Midjourney with an average amount, with Canva having barely any. Still, it can be frustrating not to get what you want after lots of work. Even more frustrating is that sometimes the best thing to do is start over. Resetting your settings to default, rethinking your prompts and beginning anew can feel like going backward. But when nothing else works, it can be a good last resort. At the end of the day, AI image generators are not replacements for creators. They're like other image editing software: You need to spend time getting to know your program, understanding how it works and its editing capabilities. Once you have a handle on your program, you'll have a good understanding of what kind of prompts deliver the best results. These tips will help get you close to what you want in the meantime. For more, check out the best AI chatbots and what to know about AI video generators.
Yahoo
07-05-2025
- Yahoo
What's More Advanced Than AI? Quantum AI Is the Next Leap Forward
Our daily workflows and routine tasks have been infiltrated by artificial intelligence, whether or not you've noticed it. Things like Gemini's integration across Google products work in the background, giving you suggestions. You might even be engaging more directly with chatbots and image generators like OpenAI's ChatGPT and Dall-E. And looming in the near future are more sophisticated virtual assistants. As if AI itself weren't futuristic enough, there's a new leap forward on the horizon: quantum AI. It's a fusion of artificial intelligence with unconventional and still largely experimental quantum computing into a super-fast, highly efficient technology. Quantum computers will be the muscles, while AI will be the brains. "My colleagues sometimes ask me why I left the burgeoning field of AI to focus on quantum computing," Hartmut Neven, founder of Google's Quantum AI lab, wrote in a December blog post introducing the Willow quantum chip. "My answer is that both will prove to be the most transformational technologies of our time, but advanced AI will significantly benefit from access to quantum computing." Here's a quick breakdown of the basics to help you better understand quantum AI. AI vs. generative AI Artificial intelligence is a technology that mimics human decision-making and problem-solving. It's software that can recognize patterns, learn from data and even "understand" language enough to interact with us, via chatbots, to recommend movies or to identify faces or things in photos. One powerful type of AI is generative AI, which goes beyond simple data analysis or predictions. Gen AI models create new content like text, images and sounds based on their training data. Think ChatGPT, Dall-E, Midjourney, Gemini, Claude and Adobe Firefly, to name a few. AI Atlas These tools are powered by large language models trained on tons of data, allowing them to produce realistic outputs. But behind the scenes, even the most advanced AI is still limited by classical computing, the kind that happens in Windows and Mac computers, in the servers that populate data centers and even in supercomputers. But there's only so far that binary operations will get you. And that's where quantum computing could change the game. What is quantum computing? Classical and quantum computing differ in several ways, one of which is processing. Classical computing uses linear processing (step-by-step calculations), while quantum uses parallel processing (simultaneous calculations). Another difference is in the basic processing units they use. Classical computers use bits as the smallest data unit (either a 0 or a 1). Quantum computers use quantum bits, aka qubits, based on the laws of quantum mechanics. Qubits can represent both 0 and 1 simultaneously thanks to a phenomenon called superposition. Another property that quantum computers can leverage is entanglement. It's where two qubits are linked so that the state of one immediately influences the state of the other, no matter the distance. Superposition and entanglement allow quantum computers to solve complex problems much faster than traditional computers. Where classical computing can take weeks or even years to solve some problems, quantum computing reduces the timeframe for achievement to merely hours. So why aren't they mainstream? Quantum computers, running on purpose-built quantum chips, are incredibly delicate and must be kept at amazingly low temperatures to work properly. They're massive and not yet practical for everyday use. Still, companies like Intel, Google, IBM, and Microsoft are heavily invested in quantum computing, and the race is on to make it viable. While most companies don't have the funds or specialized teams to support their own quantum computers, cloud-based quantum computing services like and Google's Quantum AI could be options. Is quantum AI realistic? While the potential is enormous, the main criticism of quantum AI right now is that there's a lot of hype but not a lot of realistic applications. Quantum AI faces challenges like hardware instability and a need for specialized algorithms. However, improvements in error correction and qubit stability are making it more reliable. AI Atlas Current quantum computers, like IBM's Quantum System Two and Google's quantum machinery, can handle some calculations but aren't yet ready to run large-scale AI models. Additionally, quantum computing requires highly controlled environments, so scaling up for widespread use will be a big challenge. That's why most experts believe we're likely years away from fully realized quantum AI. As Lawrence Gasman, president of LDG Tech Advisors, wrote for Forbes at the start of 2024: "It is early days for quantum AI, and for many organizations, quantum AI right now might be overkill." Quantum AI in the future Quantum AI is still in the early trial stages but is a promising technology. Right now, AI models are limited by the power of classical computers, especially when processing big datasets or running complex simulations. Quantum computing could provide the necessary boost AI needs to process large, complex datasets at ultrafast speeds. Although the future real-world applications are somewhat speculative, we can assume certain fields would benefit the most from this technological breakthrough, including financial trading, natural language processing, image and speech recognition, health care diagnostics, robotics, drug discovery, supply chain logistics, cybersecurity through quantum-resistant cryptography and traffic management for autonomous vehicles. Here are some other ways that quantum computing could enhance AI:
