AI in Processes: LLMs with Guardrails

Large Language Models (LLMs) help classify, extract, summarize, and draft. Value appears when prompts, data, and controls follow rules: clear policy, secure data boundaries, measured outputs, and a review cadence that keeps risk in check.

Overview

LLMs are probabilistic. Treat them as assistants inside a governed system: constrain inputs, retrieve from approved sources, validate outputs, log decisions, and escalate exceptions. Design the process first; then apply AI to steps where ambiguity, volume, or unstructured text slow work.

Where LLMs fit in processes

Classify & route

Category, intent, priority from emails, tickets, or docs; send to the right queue with confidence thresholds.

Extract & normalize

Pull fields from contracts, invoices, or forms; validate against schemas and reference data.

Summarize & draft

Meeting/action summaries; policy drafts; customer replies with style and policy constraints.

Prefer agent-assist (recommendations) over full autonomy for high-risk actions.

Guardrail stack

Policy & identity

  • Use policy: who may ask the model to do what; prohibited inputs
  • RBAC/ABAC; per-user logging; data minimization

Prompt & retrieval

  • Prompt templates with source citation instructions
  • RAG from approved corpora; block internet unless whitelisted
  • System prompts stored and versioned

Input & output filters

  • PII/secret redaction; file type/size limits
  • Schema validation (JSON), regex/enum checks
  • Toxicity/safety filters; jailbreak detection

Tool limits

  • Allowlisted APIs/tools with quotas
  • Dry-run mode for high-impact actions

Logging & audit

  • Prompt, context, tool calls, outputs, override/approval trails
  • Retention & access aligned to policy

Kill switch

  • Disable model/tool per user, per function, or globally
  • Rollback instructions for downstream changes

Architecture patterns (RAG, tools, approval)

Retrieval-Augmented Generation (RAG)

  • Embed approved documents; search → ground responses with citations
  • Chunking, embeddings cache, freshness policy
  • Block responses without sources for certain tasks

Tool/Function calling

  • LLM proposes parameters; platform executes tools
  • Whitelisted tools only; sandbox side effects

Approval workflows

  • Thresholds by risk: auto-approve, review, or block
  • Human signatures stored with rationale and evidence

Data, privacy & security

Boundaries

  • No sensitive data to external models unless policy allows
  • PII redaction; jurisdictional data residency; retention limits
  • Separate model context from long-term storage

Identity & access

  • SIGNED requests; per-user tokens; least privilege
  • Prompt/context access equals user’s data access

Evaluation & monitoring

Offline evals

  • Task accuracy; faithfulness/groundedness
  • Red-team prompts; jailbreak/poison tests

Online monitoring

  • Safety triggers, override rate, approval latency
  • Hallucination flags (no source), escalation counts

Drift & regression

  • Re-score on golden sets after model or data change
  • Canary/ring deployments; rollback plan

Risk & compliance

Frameworks

  • NIST AI Risk Management Framework (governance, map, measure, manage)
  • ISO/IEC 23894 AI risk management (guidance)
  • Model/system cards for transparency

Documentation

  • Purpose, scope, data sources, evaluation results
  • Known limits; escalation/appeal path

Human-in-the-loop design

Thresholds

  • Confidence × impact grid to decide auto vs. review
  • Guard high-risk actions behind approvals

Interfaces

  • Show sources and diffs; allow quick edits
  • Capture human feedback for re-eval/re-ranking

Latency, cost & performance

Levers

  • Prompt size; context window; chunking
  • Embeddings cache; result caching with TTL
  • Model routing by task; streaming vs. batch

Budgeting

  • Token budgets per user/team
  • Rate limits; quota alerts; cost per task

90-day starter

Days 0–30: Policy & scope

  • Publish AI use policy; define allowed tasks
  • Choose two low-risk use cases; set metrics

Days 31–60: Build with guardrails

  • RAG from approved sources; input/output filters
  • Logging, evaluations, and kill switch in place

Days 61–90: Pilot & scale plan

  • Run pilot; track accuracy, overrides, safety hits
  • Decide on scale; add canary deploys and rollback

References

Use LLMs where they help—and cage them with guardrails where they don’t.

If you want a guardrails checklist (policy → retrieval → filters → logging → approvals), ask for a copy.

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