Your AI Has ADHD (And Nobody Told You How to Fix It)

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You've been doing it wrong this whole time.

Not your fault. Everyone's obsessed with writing the "perfect prompt" like it's some kind of magic spell. Spend 20 minutes crafting the ideal instructions. Add examples. Tweak the wording. Hit enter.

And your AI still gives you garbage.

Want to know why? Because prompts are only half the battle. The real game is something called context engineering and it's about to change everything you thought you knew about working with AI.

Here's what's actually happening. Your AI has what I call "attention ADHD." It can only focus on so much information at once. Think of it like your own working memory. You can't hold 47 different things in your head and expect to think clearly about any of them.

Same with AI. It has a context window. A limited attention budget. And every single thing you feed it competes for that precious space.

Your prompt? That's in there. The conversation history? Taking up space. Tool descriptions? More space. Data you loaded? Even more. And here's the brutal part: the more you cram in there, the worse it performs.

Scientists call this "context rot." I call it information overload. Either way, it's killing your results.

You stuff 100 pages of documents into your AI's context window thinking "more information equals better answers." Wrong. Dead wrong. What actually happens is your AI starts losing focus. It can't find the signal through the noise. The one critical detail that matters gets buried under mountains of irrelevant data.

It's like asking someone to solve a math problem while 50 people scream random numbers at them. Good luck with that.

So what's the solution? Context engineering. It's the art of managing what your AI sees at any given moment. Not just writing good instructions but curating the entire information environment.

Think of prompt engineering as telling someone what to do. Context engineering is deciding what resources to give them so they can actually do it well.

Here's where it gets practical. You need to find what Anthropic calls the "Goldilocks zone." Not too specific where you're hardcoding every possible scenario into a 2000 word prompt that breaks the moment something unexpected happens. Not too vague where you're just saying "be helpful" and hoping for the best.

The sweet spot is giving your AI clear heuristics. Strong guidelines that are flexible enough to handle real situations.

Real world example: Instead of a massive if-else list of instructions, try something like this:

"When the user needs numerical calculations, use the Calculator tool. For text information lookups, use the KnowledgeBase tool. If you're uncertain which tool applies, ask for clarification rather than guessing."

See the difference? It's specific enough to guide behavior but flexible enough to adapt.

Now here's where this gets really powerful. You can use AI to help you engineer better context for AI. Meta right?

Try this prompt with Claude or ChatGPT:

"I'm building an AI agent that needs to [describe your task]. Here's my current system prompt: [paste your prompt]. The agent struggles when [describe specific failure cases]. Help me rewrite this prompt to be clearer and more structured, focusing on the Goldilocks zone between too rigid and too vague. Include specific examples for edge cases."

Watch what happens. The AI will help you optimize your own context engineering.

Three strategies that actually work:

Just-in-time retrieval. Don't load everything upfront. Fetch information only when needed. Your AI doesn't need to see your entire database. It needs to see the three most relevant records right now.

Compaction. Summarize aggressively. Keep only high signal information. That 10 page document? Your AI probably only needs a 3 sentence summary of the key points.

Structured note-taking. Have your AI maintain a running summary of important facts as conversations progress. Think of it as the AI taking its own notes about what matters.

Here's what blows my mind about all this. We spent years obsessing over prompts. Entire courses on prompt engineering. Frameworks. Templates. Best practices. And all along, the real bottleneck was context management.

The future isn't about writing better prompts. It's about building systems that intelligently manage what information gets loaded into your AI's attention window at the right time.

Every token counts. Every piece of information either helps or hurts. There's no neutral ground.

This is the difference between AI that frustrates you and AI that feels like magic. Between tools that kind of work sometimes and systems you actually trust with important tasks.

Start paying attention to what you're feeding your AI. Not just the prompt itself but the entire context environment. What's in the conversation history? What tools are available? What data is loaded? Is all of it necessary right now?

You'll be shocked how much better your results get when you start thinking about context, not just prompts.

The game has changed. Most people haven't noticed yet.

But you just did.

Source: https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents

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