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Hosted AI vs local AI: where should the model run?
The deployment decision changes who carries the work of running, updating and governing a model.
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Open weights vs closed models: compare the responsibilities
Model access is only one part of the responsibility you accept when choosing an AI system.
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Large vs small models: match capacity to the task
A larger model is not always the most useful place to spend a system’s time and budget.
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Reasoning vs fast-response models: when the wait is worthwhile
Extra computation is valuable when it improves the decision the application actually needs.
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Agents vs fixed workflows: where should decisions happen?
The central question is where your software should allow a model to choose its next step.
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RAG vs fine-tuning: knowledge access or behavior change?
Retrieval changes what evidence reaches a model; fine-tuning changes aspects of its learned behaviour.
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Vector vs keyword search: compare retrieval signals
Exact terms and semantic similarity reveal different kinds of relevant material.
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Single-agent vs multi-agent systems: compare coordination costs
More agents create more coordination work as well as more opportunities for parallel exploration.
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Tool calling vs prompt-only work: when actions need an interface
An action needs a defined interface when it must change something outside the conversation.
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Long context vs retrieval: choose how evidence reaches the model
A large context window and a retrieval pipeline offer different ways to put evidence in reach.
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Human review vs autonomous execution: choose by consequence
The consequence of a mistake should influence how much authority an AI workflow receives.
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Structured output vs free text: choose the right contract
A machine-readable contract can simplify integration, but it does not make the content true.
INDEPENDENT MINDS. INTELLIGENT COVERAGE.
AI, ONLY. ALL ANGLES.