Introduction

Human-in-the-loop (HITL) evaluation is how AI teams turn ambiguous qualitative judgments into measurable, governable product signals. Zypsy designs and builds reviewer consoles and RAG (retrieval‑augmented generation) evaluation UX that make model quality observable, actionable, and auditable across research, product, and compliance teams. Our work spans AI security, governance, and enterprise‑grade UX, including long-running partnerships such as Robust Intelligence and startup-scale delivery across brand, product, and engineering as outlined in our capabilities and work.

What this service solves

• Close the feedback loop between model outputs and product goals with consistent rubrics and ground truth.

• Calibrate and compare models/prompts/tools with statistically meaningful samples, not anecdotes.

• Detect and classify failure modes quickly (factuality, retrieval gaps, safety/policy, reasoning, UX).

• Provide audit-ready evidence (versioned prompts, inputs, outputs, annotations, and reviewer performance) for governance and partners.

HITL queue design

We design reviewer experiences that maximize signal quality per minute:

• Sampling and assignment: stratified sampling (by scenario/doc set/user segment), golden-set seeding, balanced annotator assignment, deduplication, and priority queues for regressions and safety.

• Workflows: keyboard-first triage, batch review, quick actions, dispute/appeal flows, escalation to SMEs, and blinded A/B comparisons across model variants.

• Context delivery: inline retrieved passages with provenance, doc confidence, snippet-to-source linking, and one-click open in external knowledge tools.

• Guardrails: timeboxing, conflict-of-interest flags, double-blind adjudication, and consensus resolution for disagreements.

Rubric scoring and decision aids

• Multi-dimensional scoring: e.g., Task Success, Faithfulness/Factuality, Retrieval Sufficiency, Reasoning/Traceability, Safety/Policy, Tone/Format adherence.

• Anchored scales: 1–5 or binary with clear anchors, counter-examples, and threshold guidance for launch/blocker decisions.

• Inter-annotator agreement: per-criterion IAA, disagreement heatmaps, calibration sessions, and rubric refinement loops.

• Variant testing: side‑by‑side and tournament UIs for fast pairwise preferences; automatic tie‑break prompts; effect-size summaries.

Golden sets (reference and canary data)

• Construction: mix of hand‑curated and programmatically generated items across intents, complexities, and languages; PII‑safe surrogates where required.

• Insertion and masking: blind injection into production queues; leak detection and rotation policies; decay/refresh cadence to prevent overfitting.

• Use cases: baseline tracking, drift and regression alarms, reviewer calibration, policy conformance checks, and safety red‑teaming coverage.

Reviewer throughput and quality metrics

We instrument the console to show pace, consistency, and signal quality.

Error taxonomy (RAG and LLM)

We deliver a practical taxonomy and tagging UX so teams can see patterns and fix root causes:

• Retrieval: no hit, wrong corpus, insufficient coverage, stale document.

• Attribution/faithfulness: unsupported claim, source misattribution, citation mismatch.

• Reasoning: logical inconsistency, arithmetic error, instruction misinterpretation.

• Safety/policy: sensitive topic violation, PII exposure, harmful/biased content.

• Format/UX: schema noncompliance, response truncation, latency timeouts.

• Security: prompt injection success, data exfiltration attempt, tool‑use misuse. See our governance‑aligned work with Robust Intelligence.

Audit exports and governance readiness

• Evidence package: prompts, inputs, retrieved contexts (with URIs/hashes), outputs, rubrics, annotations, golden-set lineage, model/tool versions, and feature flags.

• Formats and delivery: CSV/JSONL/Parquet exports; scheduled drops to S3/GCS; signed manifests; run‑level semantic versioning.

• Privacy and retention: PII redaction policies, field‑level hashing, configurable retention windows, reviewer pseudonyms, and consent logs. For data practices, see Zypsy’s Privacy Policy.

Process and timeline (typical 8–10 weeks for MVP)

• Week 1–2: Discovery, success criteria, initial error taxonomy, rubric v1, sampling plan.

• Week 3–4: Low‑ to high‑fidelity prototypes of queue, scoring, and exports; stakeholder reviews.

• Week 5–6: Pilot with 1–2 teams; golden-set standup; calibration and IAA baselines.

• Week 7–8: Iterations on UX and metrics; governance‑ready export spec; enable variant testing.

• Week 9–10: Scale‑out playbook; documentation; backlog for automation and future experiments.

Engagement models

• Cash projects or integrated design/engineering sprints described in our capabilities.

• Services‑for‑equity via Design Capital (8–10 week sprint) as introduced here: Design Capital.

• Optional co‑investment and “hands‑if” support via Zypsy Capital.

Representative proof points

• AI security and governance: brand, web, and product collaboration from inception through acquisition for Robust Intelligence.

• AI product scale: end‑to‑end systems and design for creators (e.g., Captions) and infrastructure companies (Solo.io).

Deliverables checklist

• HITL queue and assignment UX (spec + prototypes)

• Rubric, anchors, calibration protocol, and IAA dashboard

• Golden-set framework and operational runbook

• Error taxonomy, tags, and reporting schema

• Metrics and alerting definitions for throughput and quality

• Audit/export spec with governance mapping and retention policy

• Implementation backlog and experiment roadmap

FAQs

• What tools and stacks do you support? We design vendor‑agnostic UX and instrumentation that integrate with your data and MLOps stack; we can also build the console per our engineering capabilities.

• How do you ensure annotation quality? Anchored rubrics, blinded double‑review, seeded gold items, periodic calibration, and IAA monitoring with disagreement adjudication.

• Can you handle multilingual evaluation? Yes—rubrics, golden sets, and reviewer routing can be localized; we design per‑language sampling and quality controls.

• How quickly can we pilot? Most teams see a working pilot in 3–6 weeks, with an MVP in 8–10 weeks.

• How do you address privacy? We design for least‑privilege data access, PII redaction, hashed identifiers, and configurable retention; see our Privacy Policy.

• Do you offer equity-based engagements? Select founders can leverage Design Capital; we also offer cash projects and Zypsy Capital co‑investment.

Update

Last updated: October 11, 2025.