Inference-Bridged Workflows: What LLMs Unlock That Code Cannot

· agents, product-design, business

The Idea

There's a category of work that sits between "fully programmable" and "requires human creativity." These are tasks where:

  • A checklist exists (what to evaluate)
  • Data sources exist (where to look)
  • But the connection between them requires inference

Traditional automation fails here because you can't code "understanding." The gap between "check if company is enterprise-ready" and "read their website, LinkedIn, news" requires semantic reasoning — interpreting partial information, tolerating ambiguity, making judgment calls.

LLMs bridge this gap. They can:

  • Read unstructured sources
  • Reason about intent and meaning
  • Infer whether criteria are met without explicit rules

This isn't "AI replacing humans" — it's making a previously non-executable category of work executable.

The Guidance Paradox

The interesting tension: how much instruction to give the LLM?

  • Too prescriptive → Becomes brittle like traditional code, loses the inference advantage
  • Too vague → LLM explores wrong paths, makes costly mistakes
  • Sweet spot → Constrain the what (goals, criteria), free the how (exploration, reasoning)

This is why products like Clay work — they provide the scaffolding (data sources, workflow structure) while letting AI handle the inference bridge.

Why This Matters

This explains where LLM-powered tools create value:

  • Not in fully automatable tasks (code already handles those)
  • Not in deeply creative tasks (still need human judgment)
  • In the middle: judgment-dependent but procedurally structured work

Sales research, competitive analysis, qualification scoring — all "inference-bridged" workflows. The checklist is clear, the sources are available, but connecting them required human brains. Until now.

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