🕳️ Down the Rabbit Hole: The Frustrations of Using LLMs for More Than Just Text

“Just give me a quick presentation outline.” Three hours later… you’re still tweaking slides and clarifying prompts.

Sound familiar?

As powerful as large language models (LLMs) are, trying to get them to generate anything beyond plain text—like PowerPoint decks, marketing materials, or video scripts with visuals—often leads you straight into the rabbit hole: a spiral of re-prompts, misunderstood instructions, and output that’s almost right but somehow still unusable.

I’ve been down this road many times. And I’ve heard the same frustrations from other intermediate to expert AI users. So why does this happen—and is it getting any better?

🧠 Why LLMs Struggle with “Non-Text” Content

Despite how smart LLMs sound, they’re not strategists, designers, or marketers. They’re pattern recognizers trained to predict and generate text. The second you ask them to deliver something structurally or visually complex, problems arise:

• Missing Context: They don’t retain deep understanding across prompts unless you spoon-feed it.
• No Visual Reasoning: They don’t “see” a slide layout or know what makes a good infographic.
• Ambiguous Instructions: Phrases like “make it pop” or “look professional” confuse more than they clarify.
• Training Gaps: Most models weren’t trained on editable files or branded collateral—just textual descriptions of those things.

⚙️ What Actually Happens (And Why You Get Stuck)

Here’s the typical flow:

1. You make a clear request (“Give me a 10-slide deck”).
2. You get a wall of text that looks sort of like a deck—but misses your tone, structure, or objective.
3. You clarify.
4. The AI shifts direction—but overcorrects.
5. You try a different approach.
6. Repeat.

This feels like collaboration, but it often ends in exhaustion. You either settle for “good enough” or just… give up.

🔄 Even the Best Users Hit a Wall

Advanced prompt engineering can help—but it’s not a magic fix. Even with detailed instructions, sample inputs, and chained prompts, the results usually need:

• Heavy formatting
• Strategic rethinking
• Visual design intervention
• Multiple iterations

The best-case scenario? A solid first draft. But rarely a final product.

🔮 Is It Getting Better?

Yes—but slowly.

Multimodal models (like GPT-4 with vision), tool-integrated agents, and memory features are promising. They’ll eventually let LLMs interact with design tools, recall user preferences, and iterate more intelligently.

But we’re not there yet. Most LLMs still need human steering to produce useful, well-structured, and on-brand complex outputs.

💡 How to Survive the Rabbit Hole

If you’re building content with LLMs, here are some realistic best practices:

✅ Expect 3–5 iterations for anything beyond basic text. ✅ Break large tasks into smaller ones. ✅ Treat the model like a junior assistant, not a senior strategist. ✅ Know when to walk away. Sometimes it’s faster to do it yourself.

🧵 TL;DR

LLMs are amazing with text generation. But when it comes to structured, multimodal, or highly strategic content? Prepare for a time-consuming, multi-prompt journey that rarely ends with a plug-and-play result.

So the next time you ask AI to “just build a deck,” remember: You’re not just prompting. You’re falling.

🕳️ Down the rabbit hole.

🔁 Have you been down this rabbit hole yourself? 👀 Curious how others are handling complex content generation with AI? Let’s talk—drop your experience or workflow tips in the comments.