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Picking the right AI for a task
In this lesson
Picking the right AI for a task
Evaluate multiple LLM options (ChatGPT, Claude, Copilot, Gemini, domain-specific tools) against a documented decision matrix that includes cost, capability, integration constraints, and data-scope requirements for a…
You'll be able to
- Evaluate multiple LLM options (ChatGPT, Claude, Copilot, Gemini, domain-specific tools) against a documented decision matrix that includes cost, capability, integration constraints, and data-scope requirements for a given production task [^4][^6].
- Apply the Mollick seven-role framework (AI as mentor, tutor, coach, teammate, student, simulator, or tool) to classify a business or technical requirement and select the appropriate AI interaction pattern [^1].
- Justify the choice between general-purpose foundation models and task-specific augmented agents by comparing baseline LLM performance to tool-augmented architectures (retrieval-augmented generation, code interpreters, domain calculators) for accuracy-critical workflows [^3].
- Design a workload-router-pool assignment for a mixed inference fleet, mapping request characteristics (single-turn vs. multi-turn, prefill-heavy vs. decode-heavy) to routing policies and model-pool topology [^6].
- Assess when adaptive reasoning modes (such as Claude's adaptive thinking with configurable effort levels) deliver measurable accuracy or cost benefits over standard inference for complex multi-step problems [^2].