Introduction: Why rigorous RAG evaluation and HITL now

Retrieval-Augmented Generation (RAG) systems are only as trustworthy as their retrieval quality, grounding, and review loops. Zypsy designs evaluator pipelines and human-in-the-loop (HITL) workflows that quantify—and continually improve—answer faithfulness, retrieval coverage, latency, and cost for production LLM applications. We implement this with LangSmith, Ragas, Labelbox, and AWS A2I, integrated with your stack and product UX.

What Zypsy delivers

• Evaluation architecture for RAG pipelines (offline + online)

• Metrics suite and dashboards (retrieval, generation, safety, cost/latency)

• Gold datasets, rubric design, and annotation guidelines

• LLM-judge prompts, reference-based checks, and rule-based validators

• HITL workflows (task design, workforce routing, quality control, SLAs)

• CI for evaluations (nightly/PR-triggered evals, regression gates)

• Governance artifacts (model card, evaluator specs, runbooks)

• Production guardrails (PII redaction, toxicity filters, fallback routing)

• Tooling setup and handoff: LangSmith projects/runs, Ragas jobs, Labelbox projects/ontologies, AWS A2I human review workflows

Evaluator pipeline architecture

• Ingest and ground truth: curate corpora; define task taxonomy and acceptance criteria.

• Retrieval stage evals: measure coverage and rank quality before generation.

• Generation stage evals: measure groundedness, relevance, and helpfulness.

• Safety/compliance: detect PII leakage, toxicity, and policy violations.

• Cost/latency/throughput: track infra costs, token usage, and p95 latencies.

• Feedback loops: route uncertain or policy-flagged responses to HITL; use human labels to retrain/re-rank evaluators and improve prompts.

Core metrics and evaluators

Tool-specific implementation

Lang

Smith (tracing, datasets, evaluators)

• Set up projects, datasets, and run traces; attach evaluators (LLM judges, string/regex, semantic similarity) to comparisons and CI.

• Build dashboards to compare retrievers, chunkers, prompts, and models; export results for governance reviews.

Ragas (offline RAG evaluation)

• Compute RAG-specific metrics (e.g., faithfulness, answer relevancy, context precision/recall) over curated datasets.

• Integrate with LangChain/LlamaIndex pipelines for repeatable, dataset-level benchmarking.

Labelbox (human annotation and QA)

• Create ontology for pairwise answer ranking, error tagging, and rubric-based scoring.

• Stand up consensus, auditing, and annotator training; capture rationales for explainability.

AWS A2I (human review in production)

• Configure human review workflows for uncertain or policy-flagged responses.

• Use private workforce or vendor workforce; define task UIs, routing criteria, and escalation SLAs.

Representative 8–10 week sprint

• Weeks 1–2: Discovery and instrumentation

• Define use cases, policies, and success metrics; wire tracing and telemetry; extract historical logs and candidate gold questions.

• Weeks 3–4: Dataset and rubric creation

• Build stratified evaluation sets; author rubrics and acceptance criteria; pilot HITL tasks.

• Weeks 5–6: Evaluator design and calibration

• Implement retrieval and generation evaluators in LangSmith/Ragas; tune LLM-judge prompts; inter-rater reliability checks.

• Weeks 7–8: HITL workflows and dashboards

• Deploy Labelbox projects and AWS A2I flows; stand up dashboards and alerts; document runbooks and governance artifacts.

• Weeks 9–10: Production rollout and handoff

• Add CI gates, regression tests, and on-call procedures; knowledge transfer and training; roadmap for continuous improvement.

Production guardrails and governance

• Safety and privacy: PII detection/redaction, toxicity filters, domain-specific policy checks.

• Routing: fallback models/prompts; defer to human review on policy or uncertainty thresholds.

• Observability: unified logs for retrieval, generation, evaluator scores, and reviewer outcomes.

• Documentation: evaluator specs, versioning, and model cards for auditability.

Why Zypsy for RAG evaluation and HITL

• Integrated team: brand, product, and engineering under one roof for faster iteration across UX, infra, and governance. See Capabilities.

• AI/security experience: long-term partnership from inception through acquisition with Robust Intelligence; full-stack design and product work with Captions.

• Founder-native execution: sprint model, decision speed, and hands-on senior talent; optional services-for-equity via Design Capital.

FAQs

• Do I need reference answers to start? No. We combine LLM-judge and rubric-based human scoring when references are unavailable, and add references as ground truth matures.

• How big should the evaluation set be? Start with 200–500 queries across key intents; grow to 1,000+ for stable online metrics. We stratify to match real traffic distribution.

• How do you reduce LLM-judge bias? Use multi-judge prompts, randomized answer ordering, hidden references, and periodic human audits; calibrate against human gold labels.

• Can you run online A/B tests? Yes. We ship feature flags and traffic splits; evaluators compute guardrail scores while user metrics (CTR, CSAT, deflection) drive promotion.

• What about sensitive data? We isolate datasets, redact PII, and apply least-privilege access. Human tasks exclude secrets and use secure workforces with NDAs.

• Which clouds/models do you support? We work with common providers and frameworks; our evaluation and HITL patterns are model-agnostic.

Get started

• Ready to scope an evaluator pipeline or HITL review flow? Contact us via Zypsy — Contact.

• Early-stage and need both design and evaluation? Ask about Design Capital. Learn more on Capabilities.