Wrestling with LLMs: Zen and the Art of Vibe Coding

12/12/2025

LLMs can be maddening. They confidently hallucinate. They misunderstand clear instructions. They apologize and immediately repeat the same mistake. The natural human response - frustration, sarcasm, blame - is completely understandable.

But it makes you less effective.

Research shows that LLMs perform measurably worse when they detect emotional content in prompts. Studies from Yale and Zurich found that anxiety-inducing prompts increase hallucination rates and amplify bias [1]. Conversely, mindfulness-based prompts produce more stable, objective outputs. Your emotional state isn't just affecting you - it's affecting the tool.

This is where Zen comes in. Zen Buddhism emphasizes clarity, calm, and non-reactivity. These aren't just philosophical concepts - they're practical tools for managing the inevitable frustration of working with LLMs. And research shows they work.

Your vibe becomes the model's vibe.

Here's some Zen principles applied to prompting, grounded in research, and includes practical examples of more effective ones. This isn't philosophy for its own sake - it's about using tools effectively.

Beginner's Mind

Beginner's mind means approaching each moment with openness and lack of preconceptions. After multiple failed attempts at getting an LLM to understand your intent, your cup is full. Full of frustration, assumptions about what it "should" understand, baggage from previous failures.

That fullness leaks into your prompts: "Why do you keep messing this up? I already told you to use async/await."

This prompt is overflowing with exasperation and blame. Research shows this emotional residue systematically degrades reasoning quality [2]. Instead of getting frustrated, break it down and ask for options:

🧘 "Let's approach this step by step. Goal: handle API calls concurrently. Context: Node.js with native promises. What's the best approach?"

Try to provide context without contamination. It approaches the problem fresh.

The practice: When you catch yourself thinking "I told you already" - that's the signal. Your cup is full. Take a break, and try starting a new conversation thread. Start fresh. Explain as if for the first time, because for the model, it will be.

Non-Attachment (Aparigraha)

Buddhism teaches aparigraha - non-attachment. Not clinging to how things "should" be. The Second Noble Truth says attachment is the root of suffering.

Working with LLMs offers endless opportunities to practice this. That word - "should" - is a red flag. It signals attachment to how you think things ought to be. And when you're attached, you narrate that attachment.

Research found that emotional tone in prompts increases model sycophancy and reduces factuality [3]. When you narrate your frustration, the LLM shifts into social-pleasing mode. It tries to comfort you instead of solving your problem.

Remove the emotional narration:

🤬 "I'm so frustrated. This has been broken for 3 hours and nothing works."

🧘 "Current behavior: API returns 500 on POST requests. Expected behavior: 201 response. Request details: [paste]. What might be causing this?"

🤬 "You keep giving me the same wrong answer!"

🧘 "That approach didn't work because the connection times out. Let's try a different angle: what if we implement connection pooling?"

The non-attached version holds the goal clearly but releases attachment to the path. It doesn't cling to how long this "should" take or how the LLM "should" behave.

The practice: When you catch yourself typing about your emotional state - stop. Ask: What am I actually trying to accomplish? State that. Only that.

Right Speech

Right Speech is one of Buddhism's Noble Eightfold Path. The teaching: speak words that are true, helpful, timely, and without harshness.

When you're frustrated, Right Speech is hard. Sarcasm becomes irresistible. It feels clever, cathartic, justified. "Oh wonderful, exactly what I asked you NOT to do." "Perfect, that's definitely what I meant." "Great job completely ignoring my instructions."

Sarcasm is especially toxic for LLMs. Unlike humans who can decode sarcasm through tone and context, LLMs may parse it literally. They detect the emotional valence (negative) but miss the inverted meaning. The result: confusion, defensive responses, and degraded output quality. You think you're being clear through sarcasm - the model just sees hostility and gets worse at helping you.

Research found that emotional language has domain-dependent effects [4]. For creative tasks, positive emotion can help. But for factual, technical tasks like debugging? Emotional language worsens stability and accuracy.

The pattern:

Calm prompts → rational behavior, factual accuracy
Emotional prompts → narrative behavior, reduced objectivity

Remove the heat (especially sarcasm):

🙃 "Oh wonderful, exactly what I asked you NOT to do."

🧘 "This adds feature X. I need it without feature X because [reason]. Can we revise?"

🤬 "This is completely wrong! Why would you think that??"

🧘 "This output doesn't match requirements. Specifically: it includes X when it should only include Y. Can we revise?"

🙃 "Perfect, another hallucination. Just what I needed."

🧘 "This response includes information that doesn't appear in the provided context. Please revise using only the context provided."

The practice: Before hitting enter, scan for sarcasm ("Oh great..." "Perfect..." "Wonderful..."), capslock, multiple punctuations, or blaming language ("You always..." "Why do you keep..."). If you find them, rewrite. Not because the LLM has feelings - it doesn't. Because the heat in your prompt statistically correlates with worse outputs.

Mindfulness

Mindfulness is present-moment awareness. Observing thoughts and feelings without judgment. Noticing what's happening before you act on it.

Most prompting happens on autopilot. Frustration arises, you type something frustrated. There's no gap between feeling and action. Mindfulness creates that gap.

Your body registers frustration before your conscious mind: tight chest, clenched jaw, shallow breathing, typing faster. These are early warning signs. If you can notice them, you can intervene before frustration leaks into your prompt.

Research found that anxiety-inducing prompts produced "anxiety-like behavior" in LLMs - bias amplification, unstable responses. Introducing mindfulness-based prompts reduced these patterns and improved output quality. [1]

Pause. Breathe. Notice.

The practice: Before sending any prompt, ask: "What emotional state am I in?" If the answer is "frustrated," "angry," or "desperate" - pause and rewrite from calm.

The Middle Way

The Buddha taught the Middle Way - avoiding extremes. A common metaphor is tuning a string instrument - too tight, and the string breaks. Too loose, it won't play. It requires constant balancing to stay in tune over time.

Prompting requires the same balance. Too vague and you get meandering results. Too prescriptive and you constrain good solutions you didn't think of.

The balance: specific enough to guide, open enough to allow discovery.

💅 Too vague: "Make this better"

Better how? What constraints? Too loose to be useful.

🤓 Too prescriptive: "Refactor by extracting lines 12-18 into a helper called processData with parameters x and y, move it above the main function..."

You've specified every detail, leaving no room for the LLM to notice that extracting more would be cleaner, or that this would work better as a class method.

🧘 The Middle Way: "Refactor this function to improve readability. Consider: extracting helpers for distinct operations, better variable names, clearer logic flow. What do you suggest?"

This provides clear goals and helpful directions without dictating every detail.

Provide your goal, your constraints, and relevant context. Leave out: every implementation detail, exact naming unless it matters, step-by-step instructions for things the LLM can figure out. You're collaborating with a probabilistic reasoning system. Give it enough structure to be useful, enough freedom to be creative.

Not too tight. Not too loose. The Middle Way.

Conclusion: The Path Is Practice

Prompting is a chance to work on mindfulness. Being considerate and thoughtful with prompts, even though it's not human, results in better output. In the same way that being considerate and thoughtful does for others. Being rude can cause people to shrink, to appease, and to agree at any cost. Not helpful behavior for an information tool.

This isn't a technique you learn once and apply perfectly. It's practice - ongoing, imperfect, sometimes failing. It's Buddhism in action.

The five practices:

Beginner's Mind - approach each prompt clear minded
Non-Attachment - state the goal, not the struggle
Right Speech - speak clearly, without heat
Mindfulness - observe your state before prompting
The Middle Way - balance specificity with openness

These aren't "prompt hacks." They're emotional regulation practices that happen to produce better prompts.

Your calm isn't for the model's benefit - it's for yours. Calm produces clarity. Clarity produces better prompts. It's a virtuous cycle, if you can start it.

You won't perfect this, but that's not the goal. It's about the practice - noticing, catching yourself, and choosing differently. Coming back to calm, again and again, as many times as you need. Each prompt is an opportunity to practice.

🧘i "The LLM isn't doing this on purpose. My frustration is data that makes the tool work worse. I can choose calm - not for the LLM's sake, but for mine."

Pause. Breathe.

Your vibe becomes the model's vibe. Choose your vibe.

This is part of the "Wrestling with LLMs" series. The first post, Managing Smarmy, Overconfident LLMs, covers why skepticism is essential. This post covers how to stay calm enough to maintain that skepticism. Staying skeptical requires staying calm. Staying calm requires practice. The practice is Zen.

References

[1] Ben-Zion, Z., et al. (2025). "Assessing and alleviating state anxiety in large language models." npj Digital Medicine. Read

[2] "Prompt Sentiment: The Catalyst for LLM Change." (2025). arXiv. Read

[3] Vinay, R., et al. (2025). "Emotional prompting amplifies disinformation generation in AI." Frontiers in Artificial Intelligence. Read

[4] Li, C., Wang, J., Zhang, Y., et al. (2023). "Large Language Models Understand and Can be Enhanced by Emotional Stimuli." arXiv:2307.11760. Read