Enterprise RAG Evaluation Checklist for Singapore Buyers

Short answer: before you scale an enterprise RAG pilot in Singapore, score the system on retrieval quality, grounding (citations that support the answer), a versioned eval set, access-control behaviour, and a kill switch that can disable the feature without taking the product down. Fluency in a demo is not evidence.

This checklist is for AI product owners, CISOs and enterprise architects buying RAG copilots in Singapore. It invents no accuracy percentages. Pair it with the sister enterprise RAG failure-modes checklist when you want the failure cases spelled out.

Evaluation dimensions that matter

Retrieval relevance on hard cases, not only happy-path demos.
Grounding: answers cite sources the user can open and verify.
Refusal behaviour when evidence is missing or conflicting.
Latency and cost envelopes for the intended concurrency.
Access control across corpora, tools, and prompt logs.
Eval regression when indexes, models, or chunking change.

Build an honest eval set

Include known hard questions from real operators—not marketing FAQs alone.
Label expected source documents or “should refuse”.
Cover multilingual and local regulatory language where relevant.
Version the eval set; do not silently edit to make scores prettier.
Separate offline eval from production shadow traffic reviews.

Security and PDPA-aware gates

Minimise personal data in prompts and logs. Restrict who can see retrieved chunks. Align with data-residency questions and prompt-injection controls. Require a kill switch that disables the feature without taking down the whole product.

Operating model before pilot expansion

Named AI product owner and data owner.
Change board for model/index updates.
Human review thresholds for high-impact answers.
Incident playbook for harmful or wrong guidance.
Vendor exit path if the platform becomes a single point of failure.

Buyer questions for vendors

Ask how they measure retrieval failure, how they version embeddings and chunking, and who owns eval when your content changes. Map delivery to RAG copilots and AI solutions. Treat case studies as patterns, not promises.

Anti-patterns

Scaling after a single executive demo.
No eval set—only “user feedback vibes”.
Mixing public and restricted corpora without ACL tests.
Ignoring citation UX because the answer “sounds right”.
No rollback when a new index silently degrades quality.

Retrieval metrics you can explain to a steering committee

You do not need academic jargon to run a serious eval. Track whether the top retrieved chunks contain the supporting evidence, whether answers stay within those chunks, and whether the system refuses when evidence is thin. Report trends over time as you change chunking, embeddings, or corpora—not a single vanity score from launch week.

Hit-rate on must-find documents for a fixed question set.
Grounded answer rate vs fluent-but-ungrounded rate on sampled outputs.
Refusal correctness on questions that should not be answered from the corpus.
Latency percentiles at expected concurrency—not only average demo speed.
Cost per successful grounded answer, including embedding and LLM usage.

Corpus and access control tests

Mix public and restricted documents deliberately in a test tenant. Prove that users without clearance cannot retrieve or see chunks they should not. Log access. Review prompt and response logs for personal data leakage. Singapore PDPA expectations make “we will anonymise later” a weak plan.

ACL tests for at least three roles: broad employee, privileged operator, external contractor.
Red-team attempts to coax restricted content via prompt tricks.
Retention policy for logs aligned with legal hold and minimisation.
Vendor subprocessors listed for embedding and generation paths.

Pilot exit criteria before wider rollout

Exit pilot only when eval gates pass, owners exist, kill switch works, and support can explain failures. Expanding because an executive liked the demo is how fluent wrong answers become operational risk. Map build-out to AI Solutions and keep neighbouring prompt-injection and data-quality Insights in the reading list for the same programme.

Vendor diligence extras for RAG platforms

How index rebuilds are versioned and rolled back.
Whether your data is used to train shared models (contractual answer required).
Data residency options for embeddings, docs, and logs.
Export path for corpora and eval sets if you leave the platform.
Incident history handling for model or retrieval outages.

Singapore buyer framing for RAG evaluation

Singapore has no RAG-specific statute, but buyers still need a shared vocabulary for RFPs and architecture reviews.

PDPA. If indexed documents, prompts or logs contain personal data, purpose, protection, retention and access obligations still apply. Indexing can create new copies in vector stores and caches.
PDPC advisory guidance on AI recommendation and decision systems. Useful when the assistant influences decisions about individuals, not only when it summarises internal docs.
IMDA Model AI Governance Framework for Generative AI / AI Verify. Ask vendors which dimensions they tested and what evidence you will receive on each release.
Sector overlays. MAS technology-risk expectations and IM8 matter where you are regulated or public-sector adjacent. See IM8 and MAS TRM vendor questions.

Hosting changes what you can measure. A private or VPC AI deployment in Singapore often gives clearer retrieval traces and ACL enforcement than a shared public endpoint. Either way, treat eval sets and corpus releases as versioned assets under MLOps, and ask the private / VPC LLM deployment questions before you lock architecture.

Frequently asked questions: enterprise RAG evaluation

What should Singapore buyers evaluate first in an enterprise RAG system?

Retrieval quality and grounding. If the right passage is never retrieved, or citations do not support the answer, a better model will not fix it. Score those before you debate latency or UI polish.

How big does an eval set need to be?

Large enough to cover must-retrieve documents, ACL denials, empty-retrieval questions, conflicting sources and “should refuse” cases for your domain. Version it. Do not silently edit questions to make scores prettier after a release.

Does the PDPA apply to documents in a RAG index?

Yes, if those documents or the logs around them contain personal data. Decide what may be indexed at all, minimise personal data in prompts and logs, and make sure retention rules reach embeddings and caches.

When is a kill switch good enough to leave a pilot?

When a named owner can disable the assistant or a corpus path within an agreed time, without a full product outage, and the action is logged. Add a rollback for the last known-good index.

How does this checklist relate to RAG failure modes?

This page is the scoring sheet. The failure-modes checklist explains the common ways retrieval and grounding break so your eval set covers them on purpose.