The relationship between humans and artificial intelligence (AI) is changing. We are moving from rigid command-based interactions to fluid, natural communication that feels more like working with a colleague than programming a computer.
This demands rethinking how we interact with AI systems, creating a mix of structured prompting and natural communication.
In this article, you’ll get some first-hand tips on using advanced techniques and achieving more natural interactions with your AI helper through real-world examples.
Key Takeaways
- Prompt engineering is evolving into natural AI communication.
- XML tags and the scratchpad technique are important for structuring AI interactions.
- Custom AI writing styles create an “augmented” voice.
- AI research assistants now enable genuine collaborative workflows.
- The future demands conversation skills, not command mastery.
The Foundation: Structured Communication With LLMs
The real power of AI communication doesn’t come from memorizing prompts – it comes from understanding how AI actually processes our input.
Let’s begin with two techniques that are important for establishing efficient communication with AI, and more specifically, large language models (LLMs).
1. XML Tags: Speaking AI’s Language
Remember when prompt engineering felt like throwing spaghetti at the wall? Those days have long been over. XML tags are a powerful way to structure your communication with AI, making intentions crystal clear and providing a structured way for the AI to operate.
Think of XML tags as giving AI a clean, organized workspace instead of a cluttered desk.
When we use tags like <audience>, <tone>, or <context>, we organize the prompts and align them with how AI actually processes information.
The key to effective XML tagging isn’t using every possible tag; it’s choosing the right ones for your task. Essential tags include:
- <context>: Sets the background and purpose
- <audience>: Defines who you’re writing for
- <tone>: Establishes the voice and feel
- <key_points>: Outlines critical elements to cover
Check out the screenshot below to see a prompt showing how XML tags work in action.
When to Use (and When Not to)
- Creating content that needs a consistent voice
- Working on complex, multi-step tasks
- Requiring specific formatting or structure
- Quick, simple queries
- Casual conversations
- When you need rapid back-and-forth
2. The Scratchpad Method
The scratchpad technique transforms AI from a black box into a transparent thinking partner. By making AI show its work, we get better results and catch potential issues early. This also creates a much more natural AI communication style that lets you into the “mind” of the AI.
Instead of prompting and hoping for the best, we can use <scratchpad> tags to see how AI approaches our requests:
By using the scratchpad method to better your artificial intelligence communication, you can:
- Expose potential misunderstandings early
- Course-correct before the final output
- Create a genuine collaborative process
- Build on ideas through visible thinking
When to Use
The scratchpad technique can be used for:
- Complex strategic planning
- Multi-stakeholder projects
- High-stakes content creation
- Analysis requiring multiple perspectives
- Projects where reasoning matters as much as results
Next time you face a complex task, ask the AI to think it through in a scratchpad first. You might be surprised at how this simple technique changes your results.
Beyond Commands: Natural AI Communication
When we first started working with AI, everything was about trying to create a perfect prompt. That meant we might have obsessed over keywords, formatting, and precise instructions. But a shift is happening – one that changes AI from a command-line interface into a true partner.
With the new developments taking place in LLMs, AI systems can now do things like maintain context, understand nuance, and engage in genuine back-and-forth dialogue.
The key difference?
No other LLM feature for creating more natural AI communication has been as important as Claude’s new ability to learn and adapt to your unique communication style.
Not only is there an option for a formal versus casual tone, but you can actually encode your entire communication pattern into the AI’s responses.
With personal writing styles, you can now:
- Upload samples of your writing
- Train Claude (and other LLMs) to mirror your exact tone
- Capture your unique expressions and patterns
- Maintain consistency across all content
The impact on brand communication is important to note. Rather than spending hours “de-AI-ifying” content, organizations or individuals can now encode their brand voice directly into their AI interactions.
This means we are closer to:
- Every team member generating consistent brand-aligned content
- Content scaling without losing authenticity
- Maintenance of voice across all channels
- Elimination of the “generic AI” tone
As we move into 2025 and beyond, the question isn’t “How do I prompt correctly?” but rather “How do I build a genuine working relationship with AI?”
Communicating With AI Search Engines
AI-powered search engines like Perplexity are really changing how we interact with AI in general. While structured prompting techniques like XML tags remain valuable for task-oriented interactions with language models, the future of AI communication is increasingly conversational and context-aware with direct access to the internet.
Perplexity exemplifies this. Rather than relying on carefully crafted prompts, you can engage in freeform dialog, articulating your information needs in natural language.
The AI acts as an intelligent interlocutor, parsing context and intent to provide synthesized source-cited answers.
Out of all the different types of AI, these search engines are probably where you will find the most natural communication at this point.
This approach transforms the user experience from one of command and response to something more akin to a fluid research collaboration. By allowing users to express their goals and background naturally, AI search enables interactions that feel less like programming and more like consulting an expert colleague.
Consider a typical research scenario. With traditional search, you might spend significant time combing through links, skimming articles, and piecing together disparate information.
Prompt-based AI can streamline this process but still requires careful query construction and iterative refinement. Also, these LLMs are still not as good as Perplexity at querying information online.
Perplexity allows you to simply make a statement or question – “Give me the latest developments in the field of AI robotics” – and receive curated, contextually relevant information.
Follow-up questions flow organically, creating a back-and-forth that incrementally builds understanding. And on top of that, everything is backed up by online sources you can look at.
This is a far more natural way of communicating with AI, and it offers several key advantages.
Major Advantages
- Lowered barriers to entry: You don’t need to be a prompt engineering expert to get value from AI search. By excelling with simple natural language input, tools like Perplexity make AI’s power accessible to a wider range of users.
- Improved query interpretation: Context is everything in communication. By encouraging users to provide background details and engage in multi-turn dialogs, AI search can better understand the true intent behind a query, leading to more accurate and useful results.
- Enhanced knowledge discovery: Freeform interaction surfaces insights you might not think to look for via structured prompts. When you converse with AI, sometimes it might suggest related topics, counterpoints, and tangential ideas that enrich your understanding of a subject.
- Efficient information filtering: AI search acts as a smart filter, distilling the web’s vast information into concise, directly relevant snippets. This saves tremendous time and cognitive load compared to manual search and synthesis.
- Deeper, more contextual learning: By engaging in iterative conversations, users can explore topics more thoroughly, following lines of inquiry as they emerge. This organic process supports richer, more integrated learning than isolated query-response pairs.
Of course, structured prompting remains valuable for certain use cases – particularly where precise output control is essential.
But for exploratory research and knowledge acquisition, the conversational model pioneered by AI search offers a powerful new way to interact with information.
The Bottom Line
Throughout this guide, we’ve explored how AI interacting has evolved – from structured prompts to freeform conversations to personal style infusion. But the real magic happens when you blend these techniques into a seamless, adaptive communication flow.
I remember my early days with AI tools. I carefully crafted prompts and waited for outputs, but the results often missed the mark. It felt like trying to navigate without a map – lots of trial and error, with occasional lucky breakthroughs.
But as I experimented with tagging, contextual dialogue, and personal style training, a new picture emerged. Suddenly, my interactions felt less like fumbling guesses and more like fluid, natural conversations.
You can create that same experience by using all of these techniques.
Remember: There’s no singular “right” approach. The future of AI communication is dynamic, personalized, and perpetually evolving. And with the strategies you’ve learned here, you’re equipped to keep pace with it and excel.
FAQs
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References
- Use the
technique to improve your AI outputs (Ai Disruptor) - Tailor Claude’s responses to your personal style (Anthropic)
- Perplexity (Perplexity)