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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 //

Treating an AI assistant's architectural recommendation as an authoritative, final verdict rather than one input to a team decision

// Wrong: "the AI recommended microservices, so we're doing it"
// Correct: AI input + team discussion + real constraints = the actual decision

The Solution //

The AI has no visibility into real organizational context — team size, on-call capacity, political constraints, proprietary business factors — that frequently matters as much as or more than the purely technical tradeoffs it can evaluate. Weigh its input alongside team discussion and real context, not as a substitute for either.

The Error //

Requesting an architecture review with an abstract, context-free question instead of concrete details

// Low value: too abstract to produce a specific, useful answer
"Should we use microservices?"

// High value: concrete context enables a genuinely useful response
"4-person team, one feature needs 10x more compute than the rest — worth extracting?"

The Solution //

A vague question like "should we use microservices?" produces generic, broadly-applicable advice that doesn't engage with your actual situation's specific tradeoffs. Including real details — team size, the specific pain point driving the question, actual constraints — produces meaningfully more useful, situation-specific analysis.