Test streaming errors after an answer has started
Test streaming failures after partial output, including canceled connections, malformed events, recovery, duplicate text, and final task status.
Research briefing · Sources includedResearch & field notes
Research library — page 8
Read a guide, follow its sources, and check the limits before applying a finding. Search the full library from the research desk, or browse by topic.
Run a controlled LLM-provider outage drill
Run an isolated provider-outage drill that checks detection, task deadlines, approved fallback, queue recovery, and truthful user communication.
Research briefing · Sources includedTest fallback policies for provider, region, and data boundaries
Test fallback routes against approved providers, regions, data terms, capability needs, policy refusals, and exhausted eligible destinations.
Research briefing · Sources includedTest AI API-key rotation without losing the recovery path
Test AI API-key rotation through workers, caches, gateways, queues, and rollback paths using synthetic credentials and observable revocation.
Research briefing · Sources includedChoose trace sampling without hiding rare AI failures
Choose LLM trace sampling with explicit inclusion rules, failure slices, task boundaries, missing telemetry, and evaluation-use limitations.
Research briefing · Sources includedTest redaction across prompts, metadata, and error messages
Test AI redaction with synthetic markers in prompts, tool output, metadata, exceptions, and multiple exporters while preserving useful evidence.
Research briefing · Sources includedVerify observability exports preserve useful task evidence
Verify that observability exports preserve trace relationships, task identifiers, labels, versions, usage provenance, and time semantics for later review.
Research briefing · Sources includedDefine AI service objectives around completed user tasks
Define AI service objectives with task-level success, latency, evidence quality, boundaries, and explicit denominators instead of HTTP status alone.
Research briefing · Sources includedAttribute LLM cost to tasks, retries, and supporting services
Attribute AI cost to tasks, retries, retrieval, evaluation, and delayed usage while separating reported charges, estimates, and unknown amounts.
Research briefing · Sources includedRoll out a prompt version with a reversible decision
Release a prompt change with immutable versions, held-out checks, pinned conversations, and a rollback drill that restores the complete application configuration.
Research briefing · Sources includedUse feature flags for bounded AI releases
Assign AI feature exposure at a stable task boundary, test flag-service failures, and verify that disabling a flag stops the intended behavior.
Research briefing · Sources includedDesign shadow evaluation without confusing it with live acceptance
Compare a candidate AI system on copied inputs while isolating its writes, measuring duplicated workload, and labeling the limits of shadow evidence.
Research briefing · Sources includedEvaluate an AI canary release with explicit stop conditions
Evaluate an AI canary by exposure group, task outcome, critical failures, and rollback readiness instead of relying on a site-wide average score.
Research briefing · Sources includedDesign escalation rules for human review of AI work
Route AI work to human review using observable triggers, complete evidence, clear ownership, and explicit behavior when nobody can review in time.
Research briefing · Sources includedDesign a human review queue that preserves context
Build a review queue with actionable evidence, clear item states, reviewer access, reservation rules, and completion criteria tied to the decision.
Research briefing · Sources includedAdjudicate AI review disagreements with an evidence record
Resolve reviewer disagreements by inspecting evidence, rubric ambiguity, and task scope while preserving original labels and an explicit adjudication rationale.
Research briefing · Sources includedTurn user-reported AI errors into reproducible cases
Convert user-reported AI errors into permission-aware evidence, a reproducible failure, a regression case, and a verified correction without collecting everything.
Research briefing · Sources includedAsk an AI vendor for evidence you can actually verify
Assess an AI vendor’s claims with comparable task evidence, run provenance, failure examples, data-path details, and a small documented trial.
Research briefing · Sources includedScope an AI evaluation engagement with acceptance artifacts
Define a technical AI evaluation scope with a clear use case, evidence access, test populations, acceptance artifacts, exclusions, and a reproducible handoff.
Research briefing · Sources includedHow to evaluate an LLM application before release
A practical evaluation plan for LLM applications: define success, build a representative dataset, choose graders, and investigate failures before release.
Research briefing · Sources included