Decision Framework · v1.0
Which AI architecture should you actually build?
A no-fluff decision tree — before six months go into the wrong pattern.
5
Questions
6
Architectures
3
Rules of thumb
Decision tree
See every branch at once. Your path from the wizard is highlighted.
Architectures compared
| Architecture | COST | LATENCY | COMPLEXITY | Best for |
|---|---|---|---|---|
| Single LLM Call | $ | 1–3 s | LOW | Q&A, classification, content gen, chatbots. |
| Single Agent + Tools | $$$ | 5–30 s | MED | Workflow automation, code gen, research tasks. |
| Multi-Agent System | $$$$ | 30–120 s | HIGH | Complex orchestration, parallel specialists. |
| Long Context | $$ | 3–8 s | LOW | Single doc Q&A, contracts, code review. |
| RAG | $$ | 2–5 s | MED | Large knowledge bases, docs search, support. |
| Fine-Tuning | $$$$ | 1–3 s | HIGH | Domain expertise, brand voice, structured output. |
Three rules of thumb
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"Start dumb, scale up"
Always try a single LLM call first. 80% of real use cases never need more.
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"Long context killed RAG for small docs"
Under 200K tokens of static data? Just paste it. RAG is for scale, not size.
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"Agents ≠ chatbots"
If the output is a task completed, you need an agent. If it's a message returned, you don't.
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