1. Understanding Attention: Your First Step to Better Prompts
If you’re human, you’re probably reading this from left to right. You might not have stopped for a moment to consider the fact that your LLM doesn’t read in the same order as you or I. Instead, it weights relationships between all tokens at once, with position and clustering dramatically changing what gets noticed.
In working with an LLM the structure you choose can have a greater impact on your results than the precise words you choose.
As a quick example both of the prompts substantively say, the same thing. However, given the way the attention algorithm works, the results of the prompts could potentially be very different.
Why the attention-optimized version works:
The key difference isn’t the words — it’s how you structure the thinking process. By breaking down the task into numbered steps as we see in option B, you’re leveraging how transformer attention works: structured, sequential instructions create clearer context that guides the model’s reasoning.
When you use an open-ended prompt like A above the model processes multiple concepts simultaneously without clear organizational structure. It has to infer the logical flow from the prompt itself, which often leads to less comprehensive or less organized responses.
The structured version works because each step provides clear context that the model can use to organize its output. “Step 1” establishes the first focus area (threat identification), and “Step 2” defines the second focus area (mitigation requirements). This step-by-step structure helps the model understand the intended logical progression and produces more organized, thorough analysis.
This is attention mechanics in action: structure influences how the model weights and relates different concepts in your prompt. Your prompt layout shapes not just what the model outputs, but how it organizes the information.
Now let’s understand how this attention mechanism actually works…
2. Attention in ~60 seconds
The modern era of LLM development traces back to the 2017 research paper Attention Is All You Need. While modern implementations have evolved far beyond the original architecture, attention lies beneath it all. Attention is the sophisticated mathematical mechanism that enables AI coding agents and chat systems to process and relate information.
Reading the paper itself requires a corpus of background knowledge that most working software engineers simply don’t have time for. We’re going to sacrifice some precision in order to give you a simplified version that will make you better at writing prompts.
At its core, Attention is a process that calculates how much each word in a given text should influence the understanding of every other word, creating a relationship matrix.
The classic example is the cat sat on the mat . Through the attention mechanism, each word’s relationship to every other word is calculated and scored.
While the reality is that each relationship score is a complex vector of hundreds or thousands of dimensions, the essential insight is that these scores represent “how much should this word influence the understanding of that word?”
When your prompt is passed in, the meaning assigned to it is derived from a combination of the weights, the product of the statistical calculation of all inputs given during training. And the embeddings the connection of the inputs to meanings as assigned during labelling of the data.
The reason structure tends to matter more than the words you use comes down to something called embeddings. Across the training dataset, your LLM will have built up a thesaurus of words which mean the same thing. If we’d used “feline” or “kitty,” the relation to “cat” would fall roughly within the same statistical cluster.
Why This Matters for Prompt Writing
Understanding attention gives you a competitive edge: you can now predict how the model will “see” your prompts and structure them accordingly. This isn’t just about getting better outputs — it’s about developing the kind of intuition that separates good prompt writers from great ones.
For your team, this means creating prompts that consistently produce reliable, well-structured outputs. No more back-and-forth iterations or unexpected results.
So when you write a prompt, the model essentially does three things:
Converts your words to embeddings — Each word becomes a numerical representation of meaning Uses its learned weights to calculate relationships — How much each word should influence the others Generates output based on those relationships — What gets “noticed” and what gets ignored
This is why prompt structure matters so much. The model isn’t reading your words like you do — it’s calculating relationships between all of them simultaneously. Where you place information, how you group concepts, and what comes first or last all influence which relationships get stronger weights.
This is why prompt structure matters so much. The model isn’t reading your words like you do — it’s calculating relationships between all of them simultaneously
Now that you understand how attention works, let’s develop your intuition for how this plays out in real production scenarios. The best way to build this “gut feel” is to see attention patterns in action with realistic, complex prompts that engineers actually write.
3. Building Your Attention Intuition: A Production Example
3.1 The Production Scenario: Architectural Research
Let’s look at a realistic prompt that an engineer might write: requesting an AI to analyze a code base and create architectural documentation. This is the kind of complex, multi-constraint task where attention patterns really matter.
While I’ve used similar architectural research prompts in production, this specific example is crafted to demonstrate attention patterns clearly.
The Task: Create comprehensive architectural research documentation for a repository, covering tools, frameworks, design patterns, data models, API design, and identifying architectural inconsistencies. (This prompt is part of a larger AI-assisted development workflow I’ve documented in my Vibe Engineering field manual.)
3.2 A Tale of Two Prompts: Before and After
First, let’s look at two prompts designed to accomplish the same task.
Example A: The Sub-Optimal (Attention-Diffused) Prompt
Create a document 02-architectural-research.md
which details the tools, frameworks and design
patterns used across the repository.
Pay particular attention to highlight any
standards and practices that appear to deviate
from those that one would expect to find given
the recommended advice of the language and
framework.
The description should include but not be
limited to highlighting data models, api
design & versioning, as well as any other
insights that seem relevant within the
current state of the repository.
(for legacy systems)
When more than one or conflicting architecture
or pattern is being used, call this out in the
document and provide recommendations on the
best direction forward. Use references to
external sources which can qualify known best
practices from software experts.
When it is clear when there is an old or a new
way of doing things, call out which one is the
newer pattern.
Example B: The Attention-Optimized Prompt
You are a senior software architect conducting
a comprehensive codebase analysis. Your task
is to create 02-architectural-research.md
with the following structure:
CORE ANALYSIS (Required):
- Tools, frameworks, and design patterns used
across the repository
- Data models and API design & versioning patterns
- Any architectural inconsistencies or deviations
from language/framework best practices
LEGACY ASSESSMENT (If applicable):
- Identify conflicting or multiple architectural
patterns
- Recommend a best path forward with external
source citations
- Distinguish between old and new architectural
approaches
OUTPUT FORMAT:
- Use clear headings and bullet points
- Prioritize findings by impact and consistency
- Include specific examples from the codebase
- Reference external best practice sources for
any recommendations
Focus your analysis on identifying architectural
debt and deviations from expected patterns. This
is the primary goal of this research.
3.3 Comparative Analysis: Why Structure Wins
The optimized prompt produces better results because it guides the model’s attention with precision. It’s not just about what you ask, but how you ask it.
Context and Role-Playing: The optimized prompt begins by assigning a role: “ You are a senior software architect .” This immediately sets a behavioral context, anchoring the model’s tone and focus. The first prompt offers no such anchor.
The optimized prompt begins by assigning a role: “ .” This immediately sets a behavioral context, anchoring the model’s tone and focus. The first prompt offers no such anchor. Hierarchy Over Homogeneity: The sub-optimal prompt is a flat wall of text with mixed priorities. The optimized version uses clear, hierarchical sections like CORE ANALYSIS (Required) and LEGACY ASSESSMENT (If applicable) . This creates distinct attention clusters, allowing the model to tackle each part of the task sequentially without confusion.
The sub-optimal prompt is a flat wall of text with mixed priorities. The optimized version uses clear, hierarchical sections like and . This creates distinct attention clusters, allowing the model to tackle each part of the task sequentially without confusion. Front-Loaded Constraints: The most critical instruction — “ Focus your analysis on identifying architectural debt and deviations ” — is buried in the middle of the first prompt. The optimized version places it at the end as a final, reinforcing directive, ensuring it’s the primary lens through which the entire task is viewed.
The most critical instruction — “ ” — is buried in the middle of the first prompt. The optimized version places it at the end as a final, reinforcing directive, ensuring it’s the primary lens through which the entire task is viewed. Specific vs. Vague Scope: The sub-optimal prompt invites attention drift with vague phrases like “any other insights that seem relevant.” The optimized prompt removes this ambiguity by providing a specific OUTPUT FORMAT , dictating exactly how the information should be presented.
3.4 The Engineering Payoff: Maintainability
Beyond getting a better initial output, the structured prompt has a crucial long-term advantage for engineering teams: it’s easier to maintain and scale.
Think of prompts as code. The sub-optimal prompt is like a legacy script — a single block of logic where any change is risky. To add a new requirement, you have to carefully weave it in, hoping not to disrupt the flow.
The optimized prompt, however, is modular. Its segmented structure ( CORE ANALYSIS , LEGACY ASSESSMENT , OUTPUT FORMAT ) creates clear boundaries.
Need to add a new analysis point? It goes directly into the CORE ANALYSIS section.
section. Want to change the citation style? You modify a single line in the OUTPUT FORMAT .
This modularity makes the prompt more predictable and allows your team to tune constraints and add requirements without rewriting the entire instruction. You’re not just creating a prompt; you’re building a maintainable asset.
4. From Theory to Gut Feel: Practical Heuristics
The goal is to develop an intuition for how the model “sees” your requests. Think of it as a mental checklist you can run through before sending a prompt. By spotting these common attention patterns, you can predict how the model will behave and structure your prompts for success.
1. Lead with the most important thing. Always.
Models suffer from both primacy and recency bias, meaning they pay the most attention to the beginning and the end of your prompt. However, research consistently shows that instructions and context placed at the start have the greatest impact on the final output. A large-scale study by Mao et al. (2024) confirmed that an instruction’s position has a substantial effect on task accuracy.
Heuristic: If a constraint is critical, put it upfront. Don’t bury your main objective in the middle of a paragraph or tack it on as an afterthought, as it’s likely to be diluted or ignored entirely (Lou et al., 2024).
2. Structure creates focus; walls of text create confusion.
Long, unstructured prompts are a recipe for failure. Without clear boundaries, the model’s attention drifts across competing instructions and buried constraints. Using headings, bullet points, or numbered steps acts as scaffolding for the model’s reasoning process, helping it focus on one distinct concept at a time (Liu et al., 2023). This is especially crucial when your prompt contains potentially conflicting instructions, as models are notoriously bad at resolving that ambiguity on their own (Qin et al., 2025).
Heuristic: Use markdown and clear sections to break complex tasks into smaller, focused parts. This prevents key requirements from getting lost, a common failure point in long prompts (Ambassador Labs Prompting Guide).
3. Use personas for behaviour, but be careful with facts.
Assigning a role (e.g., “You are a senior software architect”) is a powerful technique for anchoring the model’s behaviour, tone, and focus, especially for complex reasoning tasks. It’s been shown to significantly improve performance on reasoning datasets (Kong et al., 2023).
However, it’s not a magic bullet. For tasks that depend on high factual accuracy, personas can be surprisingly ineffective or even detrimental. Research from Pei et al. (2023) found that personas can negatively impact factual performance.
Heuristic: Use a persona to guide the style and reasoning of the output. If you need pure factual recall, skip the persona and be direct with your query.
4. Be specific to avoid attention drift.
Vague phrases like “highlight any other insights that seem relevant” are an open invitation for the model to hallucinate or produce generic, low-value content. This phenomenon, known as attention drift, happens when the scope isn’t clearly defined. This isn’t a new problem; even the original GPT-3 paper noted that broad prompts lead to diffuse and unfocused responses compared to detailed ones (Brown et al., 2020).
Heuristic: Replace every vague instruction with a specific, measurable one. Clearly define the scope and tell the model exactly what to include and, just as importantly, what to exclude.
Developing this intuition — recognizing how your prompt’s structure will either focus or diffuse the model’s attention — is what separates engineers who get frustrated with AI from those who use it to build amazing things.
5. Attention is Your Core Economic Lever
The most significant cost of a bad prompt is squandered human focus. A single ambiguous request can easily burn ten minutes of an engineer’s time clarifying the model’s output. At scale, that wasted attention dwarfs the API cost. Your most valuable resource is engineer-hours, not GPU-cycles.
Well-structured prompts are a force multiplier, delivering compounding advantages:
Faster Feedback Loops: Focused outputs reduce retries and latency, which is critical in iterative workflows like coding and debugging.
Focused outputs reduce retries and latency, which is critical in iterative workflows like coding and debugging. Larger Effective Context Window: A lean, structured 1,000-token prompt accomplishes far more useful work than an unstructured one of the same size, making better use of the model’s working memory.
A lean, structured 1,000-token prompt accomplishes far more useful work than an unstructured one of the same size, making better use of the model’s working memory. System Portability: Modular, well-designed prompts adapt easily as new models are released, protecting your systems from brittle, expensive rework.
Modular, well-designed prompts adapt easily as new models are released, protecting your systems from brittle, expensive rework. Economic Resilience: Today’s LLM access is often subsidized. When those costs normalize, efficient prompting will become a significant competitive advantage.
Today’s LLM access is often subsidized. When those costs normalize, efficient prompting will become a significant competitive advantage. Scaling Organizational Knowledge: An engineer who masters prompt architecture can create templates and patterns that elevate the productivity of their entire team and creating compounding advantages which can scale across your organization.
An engineer who masters prompt architecture can create templates and patterns that elevate the productivity of their entire team and creating compounding advantages which can scale across your organization. Efficient Infrastructure: High-quality prompts potentially unlock powerful results from smaller, cheaper, or even open-weight models, driving inference costs down toward baseline commodity pricing of electricity and hosting.
Mastering prompt engineering builds leverage across time, latency, context, and infrastructure. The teams that treat attention as a core design discipline will consistently out-execute everyone else.
6. Closing: Attention Literacy is the Next Big-O
For decades, great engineers were defined by their intuition for what makes systems scale — mastery of complexity, memory, and latency. In an era of AI-assisted development, the primary leverage point has become the ability to precisely direct a model’s attention.
Treat prompting as systems design in natural language. Engineers who internalize this will build faster feedback loops, use context more efficiently, and run complex workflows on cheaper models. This is pure economic leverage at every layer of the stack.
Think of it this way: attention literacy is the modern equivalent of algorithmic literacy. Seeing prompts as structured systems elevates you from a passive user of AI to an active architect of its output.
Like with Big-O notation, the engineers who grasp this early will build the most significant advantages — creating cheaper, faster, and more resilient systems.
7. Further Reading
If you found the thinking here useful, you might also appreciate these other articles:
Vibe Engineering: A Field Manual for AI Coding in Teams — A guide on the disciplined practice of integrating AI-assisted coding into team workflows to build reliable and scalable systems.
— A guide on the disciplined practice of integrating AI-assisted coding into team workflows to build reliable and scalable systems. Career Advice Nobody Gave Me: Never Ignore a Recruiter — A defense of the cold-call, and a case for why the recruiters who interrupt your day are a valuable, misunderstood signal.
— A defense of the cold-call, and a case for why the recruiters who interrupt your day are a valuable, misunderstood signal. Professional Development is a Choice — On the uncomfortable truth that once you’re a professional, nobody is making you do the homework anymore — and why that’s both a risk and an opportunity.
8. References