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Pascual Vila

Full-Stack Software and AI Engineer

Full-Stack Software and AI Engineer with 6 years of experience building enterprise-grade web applications across React, Angular, Node.js, and Python. Recently completed a Master's in AI Development specializing in LLMs, RAG, and AI agent architectures, and currently builds enterprise systems that integrate AI and Digital Twins to optimize industrial and logistics processes.

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Common Pitfalls & Errors

The Error //

Asking an AI assistant to document the reasoning ("why") behind a design decision without supplying the actual context

// Risky: fabricated-sounding reasoning with no actual basis
"Document why we retry 3 times" (no context given)

// Correct: real context supplied, AI structures it clearly
"We retry 3 times because [actual historical reason]. Document this clearly."

The Solution //

The real reasoning behind a decision — a specific business constraint, a past incident, a workaround for a dependency's bug — exists only in institutional knowledge the model has no access to. Without that context supplied explicitly, a generated explanation of "why" is a plausible-sounding guess, not a documented fact, and can actively mislead a future reader who trusts it.

The Error //

Trusting generated documentation for style and clarity without reviewing it for factual accuracy against the actual code

// Insufficient review: checked for clarity, not accuracy
// Correct: verified against the ACTUAL code behavior specifically
// Does the documented parameter behavior match what the code really does?

The Solution //

A well-written, professional-sounding piece of documentation that is subtly factually incorrect about a parameter's actual behavior or an edge case is arguably more dangerous than no documentation at all, since a future reader is likely to trust and act on it without independently verifying it against the code.