ai-architecture-advisor

AI Architecture Advisor

The front-door decision skill for "I have problem X — what should I build with?" It owns the choice — which approach fits, when to promote complexity (and when not to), how to transfer knowledge, how to scale — then hands off to the deep skill that owns the depth. It spans the whole modeling space in one place so you can compare options that normally live in separate skills side by side:

tabular GBDT  ·  deep net  ·  Transformer/LLM  ·  RAG  ·  fine-tuning  ·  agents
multimodal/omni  ·  embeddings & retrieval  ·  recsys/ranking  ·  model architecture (dense/MoE/SSM/diffusion)

No theory dumps — decision tables, elimination logic, tradeoffs, and a pointer to the deep skill.

The architect's move is to ask before answering. The amateur hears "build an AI feature" and reaches for the model they know ("we'll fine-tune Kimi"). The architect first asks: what data type? what volume? what task? what's the success metric? do you even need a Transformer? The skill that distinguishes an architect is the willingness to say "for this, CatBoost wins," "here you need a Transformer," or "LoRA is enough here" — and to refuse to name an approach until the problem is classified. Never jump to a model before the Intake questions below are answered.

ASCII Flow

problem + data + constraints
  |
  v
0. INTAKE — ask before answering (see questions below)
  |          do NOT name a model until task + data + metric + constraints are known
  v
1. classify the problem          (tabular? text? generation? decision/action? retrieval?)
  |
  v
2. eliminate ineligible options  (with a reason each — never silently drop)
  |
  v
3. score survivors independently (accuracy, latency, cost, data need, interpretability, ops)
  |
  v
4. pick the SIMPLEST that clears the bar   (start simple, promote only on evidence)
  |
  v
5. hand off to the deep skill    (ai-ml-data-science / ai-llm / ai-rag / ai-agents ...)

Intake: Ask Before You Answer