LLM‑Powered Analyst Assistant — Design & Risk Checklist

Purpose and scope

LLM-powered analyst assistants can speed insight discovery, provide conversational access to operational data, and help non-technical stakeholders explore hypotheses. This checklist helps teams evaluate candidate use-cases, design safety and audit controls, measure accuracy and utility, and run short experiments that are auditable and repeatable. It focuses on prototyping safe, experiment-ready assistants rather than unconditional production rollouts.

How to use this checklist

Use the sections below to document decisions, record test results, and build a minimal viable governance plan for each assistant. Prioritize high-impact, low-risk pilot scenarios (for example, aggregated summaries of non-sensitive operational metrics) before exposing assistants to regulated or high-sensitivity data.

1. Scope & persona

• Define the target user(s) and persona (e.g., data analyst, shift supervisor, product manager). Describe skills, typical questions, and expected outputs.
• Specify the assistant's explicit responsibilities and limitations in user-facing language (what it can do and what it must not do).
• Identify decisions the assistant may influence and require human sign-off—document which outputs are advisory only.
• Classify sensitivity of expected outputs: informational, operational recommendation, or decision-critical.

2. Allowable data sources

• List data sources the assistant may query (e.g., BI warehouse, aggregated dashboards, metadata stores). Prefer aggregated/derived datasets over raw sensitive records.
• For each source, document ownership, retention, access policy, and whether the data leaves your controlled environment to third-party models.
• Disallow or require explicit approval for any source that contains PI, PHI, PII, or regulated financial records unless strong controls exist.
• Define a minimal schema/field whitelist the assistant may reference to reduce accidental data leakage.

3. Prompt safety & input/output controls

• Use structured retrieval: separate retrieval from generation. Prefer RAG (retrieve-then-ground) with explicit provenance returned to the user.
• Sanitize user inputs to prevent injection of unsafe instructions, and enforce maximum length and permitted characters where applicable.
• Limit answer types (e.g., summary, explanation, code snippet, SQL template) and avoid free-form outputs when possible.
• Ensure the assistant always returns provenance: which dataset, query, or timestamp produced the underlying evidence.
• Include a human-readable confidence statement and explicit hints when the model lacks evidence (e.g., “I could not find data for X in the permitted sources”).

4. Reproducibility & audit trails

• Log full input and output artifacts for each query: user prompt, system prompt template, retrieved evidence IDs, model response, model version, and response timestamp.
• Keep immutable records of the retrieval results (e.g., snippets or dataset query results) to allow later verification.
• Assign stable identifiers for conversations and responses to support audits and traceability.
• Define retention and access policies for logs consistent with data governance rules.

5. Evaluation matrix & success criteria

Define measurable metrics to evaluate prototypes across quality, safety, and operational dimensions. Suggested core metrics:

• Precision / Factual accuracy: % of assertions supported by provenance or corroborated by subject matter experts (SMEs).
• Hallucination rate: % of responses containing unsupported facts or invented citations (measured on a labeled test set).
• Utility / user satisfaction: Net Promoter–style or task completion scores from target users.
• Latency: median and p95 response time under expected load.
• Data leakage incidents: instances where sensitive content was exposed in model outputs.
• Fallback frequency: % of queries that triggered a safe fallback or human-in-the-loop (HITL) path.

Establish minimum acceptable thresholds for pilot success (for example: precision > 85% on core factual checks, hallucination rate < 5%, and average user satisfaction > 4/5).

6. Fallbacks & human-in-the-loop workflows

• Design explicit fallback behaviors: refuse with explanation, return limited information, or route to a human reviewer.
• For recommendation or decision-critical outputs, require asynchronous human verification before actions are taken.
• Provide an easy user control to escalate the conversation to a qualified human and track escalation outcomes.

7. Logging, monitoring & alerting

• Instrument usage metrics: queries per user, repeated queries, high-risk query patterns, and trending changes in hallucination or error rates.
• Create alerts for sensitive thresholds (e.g., an increase in hallucination rate above acceptable limits or detection of potential data leakage).
• Schedule periodic model output sampling for SME review and label results to improve evaluation datasets.

8. Access controls & governance

• Apply least-privilege access to data sources, model endpoints, and logs. Use role-based controls and request/approval workflows for new dataset access.
• Document ownership—who is the product owner, data owner, compliance owner, and SME reviewer for the assistant.
• Define acceptable use policy, user training requirements, and disclaimers for assistant outputs.

9. Retraining, model updates & versioning cadence

• Record model versioning and a controlled process for updating system prompts, retrieval indices, or model weights.
• Define retraining triggers: drift in accuracy, new data availability, or regulatory changes.
• Test updated models against a fixed holdout test set and an SME-labeled production sample before rollout.

10. Deployment & change management

• Start with a limited pilot group and a clear rollback plan. Use feature flags or staged rollouts.
• Keep an experiment log describing pilot goals, duration, sample sizes, and success criteria.
• Plan communications and training for pilot participants and a feedback loop for rapid iteration.

Quick pilot protocol (one-page)

1. Select a narrow, low-sensitivity use-case (for example: “summarize last week’s production KPIs with links to source dashboards”).
2. Document persona, datasets, and allowed fields; create a short test script of 10–20 representative queries with expected answers.
3. Instrument logging and sampling; run pilot with 10–30 users for 2–4 weeks.
4. Measure evaluation metrics (precision, hallucination rate, user satisfaction, latency). Have SMEs review a random 20% sample of responses.
5. Decide: iterate, expand, or retire. Document lessons and required mitigations before any broader rollout.

Sample prompt patterns & red flags

• Pattern — Evidence-backed summary: system prompt instructs model to cite dataset name and retrieval snippet ID for each factual claim.
• Pattern — Explain a discrepancy: require the assistant to list possible explanations and indicate which are supported by data.
• Red flag — Confident unsupported claims, plausible-sounding but unverifiable answers, or model inventing dataset names or citations.

Templates & artifacts to produce

• Use-case brief (persona, goals, datasets, sensitivity class)
• Evaluation plan (metrics, thresholds, test queries)
• Logging & retention policy
• Fallback and escalation SOP
• Pilot experiment log and SME review notes

When not to use an LLM assistant yet

• When outputs can cause regulatory non-compliance or financial loss without deterministic controls and human sign-off.
• When required evidence cannot be recorded or provenance cannot be returned reliably.
• When data policies or contracts forbid sending certain information to external model providers and no on-prem alternative exists.

References & next steps

After running the pilot, consider converting this checklist into an interactive audit or assessment so teams can record evidence, evaluation scores, and SME review artifacts (see CapabilityEnhancementNotes below for opportunities). For mature assistants, build continuous monitoring dashboards that track the core evaluation metrics and surface trends for governance review.