Agent: ChatGPT 5.2 Pro Thinking
Being an experienced software and electrical engineer who also has a ton of experience on the project management, specification, and manual writing side, is it likely that I'm getting better "vibe coding" results than usual considering that I write extremely detailed and thorough specs that break things down into a way that a human could easily complete the various coding tasks?
Yes—very likely.
“Vibe coding” quality is heavily driven by the quality of the problem formulation. If you routinely write specs that a competent human could execute with minimal ambiguity, you’re feeding the model the same kind of structure it needs to produce coherent code: clear intent, constraints, interfaces, and acceptance criteria. Your software + electrical engineering background plus PM/spec/manual-writing experience also tends to reduce two common failure modes: missing requirements and vague definitions.
That said, it’s not magic—there are predictable reasons you’d get better-than-average results, and a few gotchas that can still degrade output.
Models fill in gaps. If your spec defines:
- exact inputs/outputs,
- error handling,
- edge cases,
- performance/latency constraints,
- environment/tooling constraints,
…then the model has fewer degrees of freedom to “invent” behavior.
When you break work into independently verifiable subtasks (like a strong engineer would), you prevent the model from trying to solve everything at once and creating inconsistencies across layers (API ↔ data model ↔ UI ↔ tests).
Models respond well to “definition of done.” If you specify tests, expected logs, CLI output, or examples, you’re effectively giving it a target function.
Engineers who specify boundaries (APIs, schemas, timing, invariants) get better results than people who describe “what I want” narratively. The model can implement interfaces more reliably than it can infer them.
Even if the model’s first draft isn’t perfect, you likely:
- notice subtle mismatches,
- provide precise corrections,
- tighten the spec instead of arguing symptoms,
which rapidly converges to working code.
If the spec is extremely long, the model may:
- miss a key constraint buried mid-doc,
- inconsistently apply requirements,
- prioritize the most recent or most “salient” parts.
Mitigation: put non-negotiables at the top and repeat them in an “Acceptance Criteria” section.
If you specify exact mechanisms (rather than goals/invariants) you can box the model into a brittle design or increase contradictions. Humans ask for clarification; models tend to comply even when conflicting.
Mitigation: separate:
- must-have constraints (security, safety, timing, interfaces)
- from implementation preferences (style, patterns), and label them.
3) Hidden assumptions still bite
Even great specs sometimes assume shared context: existing repo conventions, runtime environment, deployment model, naming rules, coding standards, “obvious” domain behavior.
Mitigation: include a short “Assumptions / Out of Scope” section.
If you already write thorough specs, these additions usually produce the biggest marginal improvement:
- Concrete I/O examples
- sample requests/responses
- sample files
- expected output strings
- example timing diagrams (even ASCII)
- Acceptance tests (even informal)
- “Given X, when Y, then Z”
- explicit failure cases
- Explicit environment
- language version, OS, dependencies, constraints (no network, offline, etc.)
- A “don’t do” list
- anti-requirements prevent the model from “helpfully” adding unwanted features
- Checkpoints
- “Stop after implementing module A and show me how to run tests.” This prevents it from sprinting past errors.
You can use this as a repeatable “vibe coding” wrapper:
-
Goal (1–3 sentences)
-
Non-negotiables
- correctness, security, timing, memory, portability, deterministic behavior, etc.
-
Interfaces
- APIs, function signatures, schemas, file formats, pin mappings, protocols
-
Behavior
- normal flow + edge cases + error handling
-
Acceptance criteria
- tests, examples, “done means…”
-
Out of scope
-
Implementation notes (optional)
- preferences, patterns, style guide
-
Deliverables
- which files, documentation, commands
Then add an execution instruction like:
- “First propose a short plan and identify any spec conflicts. Then implement in small steps. After each step, list what changed and how to run/verify it.”
Given your background and how you describe your specs, it’s very plausible you’re getting materially better results than typical “vibe coding,” because you’re supplying what the model most needs: unambiguous intent, decomposed tasks, explicit interfaces, and verification targets.
If you want, paste one of your typical specs (or a redacted excerpt) and I can point out the 3–5 highest-leverage edits to make it even more model-friendly without making it longer.