5 Innovative Ways to Put Large Language Models to Work
Over the last year and a half, large language models (LLMs) have made remarkable strides—and they're only getting better. While some of the most powerful models sit behind paywalls like GPT-4 and DALL-E, plenty of new tools have become accessible to everyone. Today's LLMs handle everything from AI content creation to image processing and video editing, plus a growing array of software development and debugging utilities. The pace of AI advancement is genuinely staggering, which means it's easy to miss opportunities where these tools could genuinely save you time, money, and headaches. Let's explore five ways you can make the most of what's available right now.
Table of Contents
1. Speed Up Design Mockups with LLM-Generated Images
Image-generation tools like DALL-E deliver genuinely impressive results
Text-to-image tools have evolved dramatically and continue to improve. There are dozens of creative uses here. You can generate complete images from scratch (though they might look a bit "odd"), or use them to produce specific elements you can then refine further in traditional design software. They're particularly excellent for creating rough sketches, wireframes, and prototypes that you can iterate on by adjusting your prompts.
Free image-generation tools exist, though creative professionals often already pay for or have access to premium options like Adobe Firefly. Free alternatives can be solid—Microsoft Designer's Copilot-powered tool, for instance—but sometimes they produce lower quality or carry that telltale "AI-generated" aesthetic. DALL-E comes bundled with ChatGPT Plus and ranks among the best available. Google Gemini can generate images too, though it currently operates under certain restrictions following recent controversies.
What's interesting here is that different generation tools have distinct visual styles and capabilities, so you'll want to test a few or check out samples online before committing to any subscription (or stick with free options if one clicks with your workflow).
2. Use LLMs as Your Personal Learning Companion
You can ask LLMs even your toughest questions
One massively underrated advantage of LLMs is their educational potential. They're genuinely excellent study partners. Ask them questions on literally any topic—whether you need a quick summary or deep dives into specifics. Just pose your "why," "how," or "what makes this work" questions naturally. They also excel at generating flashcards, practice problems, and quizzes about what you're studying. For example, ask ChatGPT to create a short quiz on a topic you're learning, but request it withhold the answers. Then write your responses in the prompt and ask ChatGPT to grade you.
Of course, LLMs aren't perfect. They occasionally produce inaccurate information (a phenomenon called "AI hallucination") and can get confused on complex topics. For material that matters—whether for education or your career—the real concern is you should still prioritize checking high-quality academic sources like textbooks or lecture notes.
3. Generate Brand Logos and Visual Identity Elements
LLMs can provide finished products or valuable inspiration
LLMs can deliver fresh inspiration by generating custom images representing your brand. Logos and branding follow patterns almost by nature—every brand wants to look distinctive yet recognizable within its category, so they tend to follow design trends anyway. By describing what you need (a logo, business card, etc.) and what your brand represents (mission statement, core values), you can at least gather some novel directions to explore.
The more detail you provide, the better your results. Be specific about colors, typography, or style preferences in your prompt. Share additional context about your business too. Generate multiple image variations to harvest ideas, then ask a graphic designer to blend the elements you like best into a polished final piece. One thing to note: these tools often struggle with text-containing images, producing bizarre spelling errors or nonsensical phrases. So here's the practical tip: use the image generator to create the graphic elements for your logo or branding, then use standard photo editing software to add text, refine details, or combine components.
4. Create Templates for Any Software
ChatGPT Plus (powered by GPT-4) can generate complete Excel budget templates and output both the spreadsheet file and the Python code used to create it. While the format isn't overly complex, you can easily enhance the output by requesting minor tweaks or providing more specific instructions in your prompt.
ChatGPT already excelled at drafting job application letters and resume sections, but the ability to generate complete templates really elevates it to another level. We've mentioned using ChatGPT to improve Excel skills, but its capacity to build entire document templates makes it even more powerful and practical. These templates have limitations, sure, but they're fantastic starting points when you're stuck or unsure how to structure something quickly. They'll point you in the right direction—and even if the output doesn't match your vision, it sparks ideas for refinement.
This capability extends well beyond Microsoft Office, too. LLMs like Gemini and ChatGPT excel at generating template snippets for virtually any application that accepts templates. Because they build templates using scripts, they're not bound by file type. As long as they can write code to construct a template, they can deliver the goods.
5. Debug, Translate, and Generate Code
LLMs prove surprisingly skilled at debugging code issues—and even better at writing it from scratch
This one's fairly obvious: LLMs excel at debugging code. They can still get confused on larger projects and may easily hallucinate features or functions that don't actually exist, but for straightforward debugging work, they're fantastic. To get the best results when debugging with an LLM, provide as much detail as possible, including any specific error messages. Here's the catch though: LLMs typically store all prompts and data you send them to train future models. So if you're working in a professional environment or using proprietary information, don't feed that data to an LLM.
Another application—one that gets less attention—is code translation. Say you've written an API specification in Python but now need to support a Java SDK too. LLMs handle this remarkably well and can tackle much of the heavy lifting by converting your endpoints to another language. This use case seems less error-prone than other programming tasks. Beyond that, you can use LLMs to generate boilerplate code. No need to hunt through GitHub for a project starter template anymore. An LLM can do that work for you, handling package manager setup, dependency libraries, and even basic CI pipelines.
How Creative LLM Applications Save Time and Effort
LLMs grow more capable by the week. Like any powerful tool, we need time to learn how to wield them effectively. Here we've focused on what you can do with major LLMs currently available—ChatGPT and Gemini—but as more specialized tools launch, even more creative applications will emerge. For now, there's plenty we can do to unlock the obvious potential of these remarkable tools.
Description: Discover practical applications of LLMs that can save you time and effort, from design mockups to code debugging.
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