Conciseness

The body is dense with PostHog-specific domain detail (settle strategies, globals, provider-key gating) that Claude would not already know, and largely assumes competence rather than padding; it earns 4 over 5 because a few prose sections (e.g. the session-target bounds and skipped-evaluation mechanics) could be tightened or moved to the reference, and over 3 because it is not "noticeably verbose" with generic explanation.

Actionability

Provides copy-paste-ready JSON payloads for hog and llm_judge eval creation, exact `conditions` shape, a runnable SQL verification query, and concrete `generate-app-url` calls — covering the common cases per the score-5 anchor.

Workflow Clarity

Sequences a two-phase process (1.1–1.3 then 2.1–2.6) with an explicit validation checkpoint (2.5 verify scope via SQL before enabling) and feedback loop (sample, review first live results, adjust rollout), satisfying the score-5 anchor and avoiding the batch-operation cap since validation is present.

Progressive Disclosure

Structure and sectioning are good, but the body twice signals [references/evaluation-payload.md](references/evaluation-payload.md) as the home for "every field, the config schemas, the exact conditions shape" and that file does not exist, so navigation is broken; additionally reference-grade detail (session settle bounds, the globals table) is inlined rather than split out, matching the score-3 anchor's "references present but not clearly signaled / content that should be separate is inline" better than the 4 anchor's "minor organization gaps".

Specificity

Lists multiple concrete actions spanning the whole workflow — "choosing the target and eval type (hog / llm_judge / sentiment)", "configuring a provider and model", "scoping which generations trigger it via conditions", "creating disabled, verifying scope, and enabling" — giving comprehensive coverage, matching the score-5 anchor rather than the 4 anchor's "minor gaps".

Completeness

Explicitly answers both what ("Author continuously-running online evaluations in PostHog AI observability") and when ("Use when the user wants evaluations that automatically score new generations or whole traces going forward") with concrete trigger phrases, matching the score-5 anchor exactly.

Trigger Term Quality

Includes natural phrases users would say — "create an eval to catch X", "continuously check that responses do Y", "turn these failures into evals" — giving good keyword coverage; falls short of 5 because it lacks the broader synonym/extension breadth the top anchor calls for, and short of the "missing common variations" gap that defines 3.

Distinctiveness Conflict Risk

Occupies a clear PostHog-AI-observability niche with distinct triggers and explicitly redirects overlapping jobs to siblings ("use exploring-ai-failures first", "use exploring-llm-evaluations"), minimizing conflict risk per the score-5 anchor.