The Sour Lesson: Building AI Products That Compound with Model Progress at Anterior
Building AI products on rapidly evolving LLMs creates a tension: serving customers today requires working around current model limitations, but those workarounds become technical debt when capabilities improve.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Medical review case arrives
A typical medical review requires processing clinical guidelines and patient medical records.
Tools used
GPT-3.5-Turbo-16kGPT-4 TurboGPT-4Claude 3
Outcome
Domain knowledge injection and the expert review system both remained in production 2+ years after being built. The earlier hierarchical approach, while short-lived, helped acquire the first 2 enterprise customers within 1-2 months.
What failed first
Hierarchical query reasoning—breaking medical guidelines into tree-structured sub-questions answerable within a 16K token window—became unnecessary when GPT-4 Turbo launched with a 128K context window. Finetuning for clinical reasoning was similarly superseded within 12-18 months as frontier general models became strong enough to eliminate the advantage of specialized models; the hierarchical approach itself was replaced within 6 months.