Your AI coding assistant isn’t losing its mind; it’s just lacking a manager.
We have all experienced the degradation of code quality when working with LLMs. The first iteration is magical, the second is functional, but by the fifth or sixth turn, the project dissolves into a “dumpster fire” of conflicting logic and broken dependencies. I recently came across a brilliant breakdown by a Reddit user, Creative_Source7796, who diagnosed this exact problem. The creator pointed out that when we ask an AI to “build feature X,” we are forcing it to simultaneously act as the Product Manager and the Software Engineer. This split focus leads to the model inferring requirements on the fly, which inevitably causes drift.
The core strategy shared by this savvy professional is simple but rigorous: never let the AI write a single line of code until it has written a comprehensive product spec in plain English. Instead of letting the model guess the constraints, the author forces it to define them explicitly. This approach separates the “thinking” from the “coding,” ensuring that the architectural logic is sound before any syntax is generated. It essentially forces the LLM to “measure twice, cut once.”
💡 Insight 1: Establish the “Why” and “Who” Before the “How”
The original poster found that skipping the context phase is fatal for long-term project health. They discovered that forcing the model to answer foundational questions changes the entire trajectory of the code.
Contextual Grounding: The author insists on asking specific questions like “Who is this feature for?” and “What problem does it solve?” before requesting code. This grounds the AI in the user’s reality rather than the code’s abstract logic. By defining the persona and the pain point, the model generates code that is actually aligned with the user experience rather than just syntactically correct.
The “Human Dev” Filter: A particularly clever tactic this expert uses is asking the AI, “What would be unclear to a human developer reading this spec?” This forces the model to perform a self-audit. It identifies ambiguity in its own plan and resolves it before that ambiguity turns into a bug. It’s a meta-cognitive step that significantly reduces the error rate in the final output.
✅ Insight 2: The Power of Negative Constraints
One of the most valuable findings from this post is the importance of defining what not to do.
Explicitly Out of Scope: The creator asks the model to list what is “explicitly out of scope” and what should “not be included in v1.” This is crucial because LLMs are people-pleasers; they tend to hallucinate extra features or handle edge cases you didn’t ask for, just to be helpful.
Stopping “Feature Creep”: By forcing the AI to list what it will ignore, the author effectively puts blinkers on the model. This keeps the implementation focused and prevents the “bloat” that often breaks AI-generated projects. The model stops inventing complex edge case handlers for scenarios that don’t exist in the current version.
📌 Insight 3: Enabling “Vibe Coding”
This workflow unlocks a more hands-off coding style the author refers to as “vibe coding.”
Trust through Clarity: Because the plan is verified in plain English first, the author found they could trust the code generation phase much more. They mentioned being able to build projects without reading every single line of code in depth because the logic had already been validated.
Coherence over Time: The expert noted that implementation plans became much more coherent. Even when using advanced tools with “agent plan modes,” fuzzy intent leads to optimization of the wrong things. Clear text specs act as an anchor, preventing the model from drifting away from the original goal as the context window fills up.
How to Apply This Workflow
Based on the guide provided by the original poster, here is how you can restructure your prompting strategy to mimic this success. You need to break your interaction into three distinct phases.
Phase 1: The Product Spec Prompt
Do not ask for code yet. Run this prompt first to establish the ground rules:
“Before writing any code, write a short product spec for what this feature is supposed to be. Who is it for? What problem does it solve? What is explicitly out of scope? List assumptions you’re making about users and constraints.”
Phase 2: The Implementation Plan
Once the spec is generated and you have verified it looks correct, move to the planning phase. This allows the AI to think through the architecture without the distraction of syntax:
“Now plan how you would implement this. What would be unclear to a human developer reading this spec?”
Phase 3: The Execution
Only after the plan is solid do you give the green light for the actual build:
“Now write the code.”
This is a fantastic reminder that natural language programming is still programming: you have to define your variables and scope before you execute the function!
Check out the full discussion and the author’s guide for more examples.
💡 FAQ & Troubleshooting
Why does AI code generation often degrade after 5-6 iterations?
This typically happens when the model is forced to infer product requirements while it is already building. If the product intent is fuzzy or not explicitly defined beforehand, the model effectively “makes up” requirements as it goes, leading to incoherent code and a “dumpster fire” result as the context grows.
What is the recommended workflow to ensure high-quality code outputs?
Instead of immediately asking the AI to “Build X,” follow a strict three-step sequence:
1. Spec: Ask the AI to write a short product spec (defining the user, the problem, and what is out of scope).
2. Plan: Ask the AI to plan the implementation based on that spec.
3. Code: Only after the plan is set, ask the AI to write the code.
How can I stop the model from inventing extra features I didn’t ask for?
You must explicitly define constraints before coding begins. Use prompt patterns such as “What is explicitly out of scope?” and “What should not be included in v1?” Additionally, ask the model to list the assumptions it is making about users and constraints to ensure it isn’t hallucinating requirements.
What prompts help clarify a complex feature before coding?
To ensure the AI understands the task, ask it to “Write the product requirements in plain language” and answer the question: “What would be unclear to a human developer reading this spec?” This forces the model to identify ambiguity before it attempts to write code.
I stopped asking AI to “build features” and started asking it to spec every product feature one by one. The outputs got way better.
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