Why Large Language Models Skip Instructions and How to Address the Issue

Large language models, or LLMs, have grabbed the spotlight in AI over the past couple of years. They’re everywhere—helping people write, code, research, and even run support chats. Yet for all their talent, even the most advanced models, like GPT-4 and Claude-3, aren’t flawless. If you’ve ever fed one a big, complicated prompt and felt like part of your request just vanished into thin air, you’re not alone. Sometimes, these AI assistants stumble when following multi-step or complex instructions, leaving you with answers that are partial or a bit off the mark.

Why Do These Models Skip Parts of Your Instructions?

At their core, LLMs process everything you write by breaking it down into tiny bits called tokens, then respond one token at a time. Because of this sequential logic, what you put at the start of your prompt often gets more love than what comes after. If you keep piling on instructions, the details at the end might get muddled, misunderstood, or skipped entirely.

There are other factors, too. The attention mechanism—essentially how the model decides what to focus on—works great for short and clear prompts. But as your instructions get longer and more tangled, that focus spreads thin. This “information dilution” means the model has a harder time keeping track of every request you make. Plus, these AIs are usually trained on simple, single-step questions, so they develop a knack for those and can struggle with tasks that are more layered or detailed.

Then there’s the brute reality of token limits. Every model has a ceiling—give it a prompt that’s too long, and anything past that limit simply won’t make the cut. The model can’t “see” what’s beyond its reach, so those instructions might as well not exist.

How to Get Better, More Complete Answers

Here’s the good news: while LLMs have these shortcomings, there are practical workarounds. Start by chopping up big, complex prompts into smaller, clearer steps. Using bullet points or numbering helps the model treat each item individually, rather than burying requests in paragraphs.

Be direct and explicit. If you want the AI to do every step, say so—don’t leave room for ambiguity. And if you need the model to nail a very precise answer, it’s sometimes best to break the task over several prompts, handling one part at a time.

If you want to keep things moving efficiently, there are advanced approaches like “Chain-of-thought” prompting, which encourage the model to reason through problems step by step. You can also batch requests, use consistent labels, and keep formatting crystal clear to help the AI process each instruction without confusion.

Different models have their own quirks—what works with one might fail with another. Try running your prompts past multiple models, tweaking things like format and length, or even fine-tuning the model (if you’re tech-savvy). In some cases, linking up external tools or services to your workflow can give your results the extra accuracy you’re looking for.

So yes, instruction skipping is a quirk of LLMs that can be frustrating. But knowing how these systems work—and being willing to experiment a little—means you can almost always get them to deliver better, more complete results, whether you’re putting together a blog post, crunching some data, or building your next app.

If you want a deeper dive into why LLMs behave this way and what else you can do, check out the original article here.