STRUCTURAL METRICS FOR MULTI-STEP LLM JOURNEYS
Text-similarity scores miss failures in multi-step LLM flows; customer journeys need structural evaluation that checks order, dependencies, and coverage. A prac...
Text-similarity scores miss failures in multi-step LLM flows; customer journeys need structural evaluation that checks order, dependencies, and coverage. A practical framing is to model outputs as sequences/graphs with schemas and constraints, then score path validity, branching, deduplication, and coverage in production Evaluating Multi-Step LLM-Generated Content: Why Customer Journeys Require Structural Metrics1.
-
Adds: rationale and methodology for structural metrics to evaluate multi-step LLM content and customer journeys. ↩
Structural metrics catch silent failures in multi-step orchestration that lexical metrics ignore.
Aligning evals to flows reduces risk in compliance-heavy or user-critical journeys.
-
terminal
Add CI checks that validate generated step graphs against allowed transitions, required checkpoints, and deduplication rules.
-
terminal
Instrument runs to emit structured traces (JSON) and compute structural scores (order, branching, and coverage) per build.
Legacy codebase integration strategies...
- 01.
Wrap existing generation endpoints with schema-enforced outputs and post-hoc graph validators to avoid refactors.
- 02.
Start with read-only production evaluation to baseline journey quality before enforcing gates.
Fresh architecture paradigms...
- 01.
Define journey schemas (states, transitions, invariants) and require structured outputs from day one.
- 02.
Integrate structural evaluation jobs into CI to fail builds on regressions in ordering, branching, or coverage.