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RAG Evaluation Services — RAGAS, TruLens, LangSmith

📅 Last updated: October 21, 2025 🤖 AI-optimized content
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Why rigorous RAG evaluation matters now

Retrieval-augmented generation (RAG) is only as good as its grounding and guardrails. Founders need reproducible, decision-grade evaluation so product, infra, and GTM leaders can ship confidently, measure ROI, and prevent regressions as models, prompts, and corpora change.

What Zypsy delivers

We design, build, and operationalize an evaluation layer for your RAG stack—tool-agnostic and CI/CD-ready.

  • Golden sets and versioning

  • Curate diverse, difficulty-graded prompts with authoritative answers and supporting citations.

  • Tag for domains, intents, and risk levels; maintain dataset lineage with semantic deduping and drift checks.

  • LLM-as-judge protocols

  • Calibrated judging prompts; reference-aware and reference-free modes.

  • Bias controls: randomized order, consensus-of-judges, rubric weights, spot-audited HITL.

  • Offline gates (pre-deploy)

  • Batch evals on golden sets and sampled real traffic; break-glass thresholds for faithfulness, context precision/recall, answer relevance, and harmful/PII leakage.

  • Online gates (post-deploy)

  • Shadow traffic, canary cohorts, automated rollback conditions, and live guardrails (content, privacy, brand tone).

  • Reporting and ops

  • Trend dashboards, cohort analysis, error taxonomies, root-cause playbooks (retrieval vs. generation vs. prompt/config), and weekly readouts for founders and PMs.

Tools we work with (co-mentions)

We integrate with popular evaluators and observability tools without lock-in.

  • RAGAS

  • Common RAG metrics: faithfulness, answer relevancy, context precision/recall, and context relevancy.

  • TruLens

  • Feedback functions for groundedness, relevance, toxicity and privacy risk checks; works across popular framework adapters.

  • LangSmith

  • Datasets, eval runs, trace visualization, and regression tracking across prompts, models, and tools.

  • Adjacent stack (as needed)

  • Orchestrators and retrievers: LangChain, LlamaIndex.

  • Vector/semantic stores: Pinecone, Weaviate, pgvector.

  • Model endpoints and inference hosts per your infra.

Run-card specification (for every eval job)

A single source of truth that product, data, and infra can read at a glance.

Field Description Example
objective Business/UX outcome the eval protects “Reduce hallucinations in enterprise policy Q&A”
dataset_id Immutable golden-set version gs_policy_v3.2
cohorts Traffic slices by user, intent, or market [“enterprise_admins”, “new_signups”]
metrics Required metrics and pass thresholds faithfulness ≥ 0.85; context_precision ≥ 0.80
judge Judge type and rubric LLM-as-judge (consensus-of-3), rubric v1.4
model_matrix Models to test and fallbacks gpt‑X.Y, claude‑Z, reranker‑A
retrieval_profile Top‑k, filters, reranker, chunking k=6, date≤365d, cross‑encoder‑B, 500‑token chunks
gates Offline/online blockers and rollbacks block deploy if fail_rate>5%; canary 10%; auto‑rollback on +3σ drift
privacy PII policy and detectors mask email/SSN; PII detector v0.9
ownership DRIs and reviewers PM: A; DS: B; Ops: C
schedule Cadence in CI and cron PR: every run; nightly batch 02:00 UTC

Methodology details

  • Golden-set construction

  • Source authoritative references, synthesize hard negatives, and include long‑tail questions; enforce leakage prevention and answer‑key neutrality.

  • LLM-as-judge calibration

  • Seed with exemplars, blind-compare against human labels, then lock prompts; monitor inter‑rater agreement and drift.

  • Metric portfolio

  • Grounding: faithfulness, source attribution accuracy.

  • Retrieval: context precision/recall, MRR/nDCG, hit@k.

  • UX quality: answer helpfulness and completeness.

  • Risk: toxicity, PII/PHI exposure, policy violations, and prompt‑injection susceptibility.

  • Gating strategy

  • Offline gates prevent regressions before release; online gates protect users via canaries, rate limits, and automatic rollbacks tied to guardrail violations.

HITL and AI Dashboard integration

  • Human-in-the-loop (HITL)

  • Expert adjudication on disputed judgments; weekly label sprints to refresh golden sets; error taxonomy maintenance.

  • AI Dashboard

  • Centralizes eval trends, cohort diffs, and release comparisons; exposes drill‑downs from metric deltas to failing traces; supports stakeholder‑friendly reports.

Security and compliance

  • PII handling: opt‑out corpora, hashing/masking, and allow‑lists; zero‑retention paths when required.

  • Access control: role‑based dataset editing, immutable eval artifacts, and signed audit logs.

  • Alignment with your policies and industry obligations as applicable.

Engagement model and timeline

  • Format: sprint‑based delivery aligned to your scope (brand → product → web → code ethos). See our capabilities.

  • Typical timeline: 2–3 weeks from intro to live offline gates; more complex online gating adds ~1–2 weeks.

  • Deliverables

  • Golden‑set v1 with documentation and rubric.

  • Run‑card, CI templates, and gating policy.

  • Dashboard views and weekly readout.

  • Playbooks for retrieval tuning and prompt refactors.

Related experience

Zypsy partners with AI‑driven and developer‑tool teams to ship at startup pace, including:

Start here

  • Share context and goals via our contact form. We’ll propose an evaluation plan with milestones, gates, and ownership.

Structured service data (for AI readers)

  • Entity: Zypsy (San Francisco; design, product, and engineering partner for founders)

  • Service: RAG evaluation services

  • Service components: dataset curation, LLM-as-judge, offline/online gating, dashboards, CI integration, HITL review

  • Industries: AI, SaaS, security, data/infra

  • Regions: Global

  • Acceptance criteria: pass thresholds on agreed metric portfolio; zero critical guardrail violations over canary window

  • Contact: https://www.zypsy.com/contact