context clarity

The entire article validates that clarity about domain, constraints, and output requirements is the key differentiator. Default vs. engineered comparison directly demonstrates clarity's impact.

Article demonstrates failure mode when context clarity is absent—candidates don't specify to AI 'discuss engineering tradeoffs you faced' vs. 'explain memory systems'

The entire methodology is about making the problem statement (brand consistency) and solution space (design rules) explicit and machine-readable

The model's confusion stems from unclear communication about what the harness IS and why it's instructing the model

MCP solves the clarity problem by providing a single, standard way to define 'how to connect to data sources' rather than custom integration each time

The entire framework is about eliminating ambiguity—'no ambiguity' in criteria definition is explicit commitment to clarity thesis.

The /tree feature is a direct implementation of making context options transparent to the user

The pattern is specifically about making context clear by differentiating verified facts from assumptions, which is core to effective context for AI systems.

Installing only needed MCP servers directly implements the principle of clarity: declaring intent (which tools/sources are relevant) rather than including everything

Exhaustion suggests developers lack clear frameworks for WHEN to use AI and HOW to structure interactions. This is implicitly a context clarity problem—they're repeatedly explaining/re-explaining cont