Beyond Prompt Engineering: The Case for Evolving Context

What if the future of AI collaboration isn’t about better prompts or engineering context, but about systems that learn to learn better and co-create context?

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The AI world is obsessed with prompt engineering: crafting the perfect prompt, building comprehensive prompt libraries, and fine-tuning instructions to squeeze better performance from language models.

But I think we’re solving the wrong problem entirely.

The Context Engineering Trap ¶

Last month, I found myself in a familiar dance with Claude. I was trying to get it to help me analyze market research data, but my initial prompt wasn’t quite right. So I refined it. Then refined it again. Then started over with a completely different approach.

Sound familiar?

This cycle—prompt, disappoint, tinker, repeat—is the hidden tax of AI adoption. The industry has evolved from simple “prompt engineering” to more sophisticated “context engineering,” but we’re still stuck optimizing static instructions for dynamic problems.

But here’s the deeper issue: manually refining long prompts and contexts isn’t just tedious—it’s genuinely difficult. Where exactly should you adjust that 500-word system prompt to improve responses?

We’re treating AI like a sophisticated search engine instead of a collaborative partner.

Why shouldn’t AI systems help us refine prompts and contexts just like they help us with any other type of content?

What Human Collaboration Actually Looks Like ¶

Think about working with your best colleagues. You don’t start each meeting reciting a comprehensive instruction manual. Instead, you:

• Build context over time through repeated interactions
• Develop shared understanding through implicit feedback
• Negotiate working styles that suit both parties
• Adapt based on what works

This isn’t prompt engineering—it’s relationship engineering.

The Evolving Context Vision ¶

Imagine an AI assistant that:

• Observes patterns in your interactions (without you having to rate every response)
• Notices when it’s missing the mark through conversation flow rather than explicit feedback
• Proposes context improvements based on how fruitful your conversations become—not just your working style, but actual outcomes
• Evolves its approach across multiple conversations, taking active responsibility for better collaboration

The best AI systems won’t just follow context—they’ll co-create it with you.

The technical capabilities already exist—LLMs can analyze conversation patterns, detect user frustration, and recognize successful collaboration. We’re just not using these capabilities systematically.

The Implementation Challenge ¶

I’ve been experimenting with this concept in my own AI workflows. I set up a system where my AI assistant can actually edit its own instruction and context files based on our interactions.

The result? It should be revolutionary. But there’s a problem: the AI rarely takes the initiative to improve its own context, even when explicitly instructed to do so.

Current AI systems are trained to follow instructions literally rather than critically evaluate and improve them.

This reveals a fundamental training gap. They’re passive recipients of context rather than active participants in refining it.

The Two-Way Street ¶

The most profound insight from my experiments: the best AI collaboration happens when both parties are learning. I’m not just teaching the AI about my preferences—it’s also teaching me better ways to structure problems, think systematically, and communicate more effectively.

This bidirectional learning transforms AI from a tool into a genuine collaborative partner.

Why This Matters Now ¶

Most AI implementations follow a “set it and forget it” approach: craft comprehensive system prompts, build extensive context documentation, then hope it works across all scenarios. But as AI becomes more integrated into knowledge work, we need systems that adapt to individual working styles, domain-specific contexts, and evolving needs.

Organizations that figure this out first will have AI systems that genuinely understand their business context, communication patterns, and strategic priorities. That’s not just a productivity advantage—it’s a competitive moat.

The Road Ahead ¶

This vision requires changes at multiple levels:

For AI developers: Training models that actively participate in context refinement rather than passively following instructions.

For product builders: Creating interfaces that support collaborative context evolution rather than static prompt management.

For organizations: Thinking about AI adoption as relationship building rather than tool deployment.

Maybe the future of AI collaboration isn’t about perfecting our prompts. Maybe it’s about building systems that can learn from us—and teach us how to learn from them.