AI's Preference for Simple Rules Over Intelligence

AI coding assistants consistently produce hardcoded solutions for tasks requiring judgment, like classifying project documents into categories such as standards, drawings, specifications, contracts, or general notes. Instead of using LLMs for contextual analysis, they default to keyword dictionaries and string matching. This solves the immediate problem but creates brittle code that fails on edge cases, as it treats intelligence problems without actual intelligence.

To classify from title and description, the AI outputs:

DOCUMENT_TYPES = {
    "spec": "specification",
    "drawing": "drawing",
    "standard": "standard",
    "contract": "contract",
    "agreement": "contract",
    "scope": "scope",
}

def classify_document(title, description):
    text = f"{title} {description}".lower()
    for keyword, document_type in DOCUMENT_TYPES.items():
        if keyword in text:
            return document_type
    return "general"

This generates functional code in under a minute but relies on exact keyword presence, ignoring synonyms, context, or ambiguity.

Developer Workflow Fix: Review and Refactor

The real work starts post-generation: developers must spot assumptions in the code, like rigid mappings (e.g., "agreement" and "scope" as "contract" or separate). Refactor by prompting for LLM-based classification to handle nuance, such as embedding text and cosine similarity or direct LLM prompting for categories. This pattern repeats often, so always audit AI outputs for over-simplification—quick wins hide scalability issues.