Practical patterns, guardrails, and playbooks to safely apply AI and automation for capturing knowledge, improving search, and scaling routine decisions.
A practical, interactive checklist for designing safe, explainable retrieval-augmented assistants. Guides teams through scoping, data sources, retrieval strategy, prompt templates, human-in-the-loop controls, validation tests (including simple hallucination detection), logging, access controls, and monitoring.
Practical guardrails, retrieval and prompt patterns, provenance practices, human-in-the-loop handoffs, evaluation metrics, privacy controls, and a brief rollout runbook for building retrieval-augmented assistants that support organizational knowledge work.
A practical, actionable guide for designing retrieval‑augmented, role‑aware assistants that surface institutional knowledge safely. Includes architecture patterns, prompt templates, provenance and explainability approaches, guardrails to reduce hallucination and data leakage, update/retirement workflows, monitoring metrics, escalation rules, and two concrete example flows (employee onboarding Q&A and incident triage).
Practical guidance for when and how to apply retrieval-augmented generation (RAG) to make organizational knowledge findable, traceable, and defensible — plus architecture patterns, implementation checklist, short prompt templates, and a failure-mode checklist.
Playbook for designing automated alerts and routines that escalate to owners with clear context and learning steps, while preserving human review and audit trails.
Specification template to design retrieval-augmented agents and assistants that safely surface organizational knowledge.
Practical playbook with decision checklists, human-in-the-loop agent templates, audit and explainability requirements, monitoring & rollback runbook, governance guardrails, and ready-to-adapt sample micro-automations (daily KPI summaries, ETL routines, repository curation). Designed to help teams scale routine work while preserving learning, ownership, and safety.
A practical, field-ready spec template and checklist for designing, implementing, testing, and operating retrieval‑augmented agents that connect to internal knowledge with clear rules for retrieval, scoring, citation, safety, monitoring, and failure handling.
A practical playbook for selecting, designing, testing, deploying, and operating lightweight agents (summarizers, note-takers, alert actors, ETL assistants) with explicit human oversight, audit trails, safe defaults, rollback plans, and measurement strategies to keep learning and ownership intact.
A practical, step-by-step playbook to design, build, evaluate, and operate retrieval-augmented assistants (RAAs) for organizational knowledge. Covers use-case mapping, index design, chunking, provenance, prompt patterns, privacy and hallucination guardrails, human-in-the-loop escalation, monitoring, and a rollout checklist with lightweight evaluation metrics.
Practical patterns, templates, and governance guardrails for automating routine alerts, ETL jobs, scheduled reports, and multi-step workflows while preserving human review, learning loops, ownership, auditability, and safe rollback paths.
A pragmatic, step-by-step assessment template teams can use before building, buying, or deploying AI or automated decision tools. Includes clear sections, suggested questions, evidence to collect, a lightweight scoring rubric, sign-off criteria, and post-deployment monitoring guidance that organizations can adapt.
An actionable, savable checklist teams can use to assess readiness before introducing AI-assisted tools into workflows. Includes fields to capture approvals, effort estimates, risk scoring, ownership, and notes to support prioritization and follow-up.
A practical, reusable matrix-style checklist teams can use to evaluate privacy, bias, explainability, data minimization, security, human oversight, vendor risk, and operational safety before advancing an assistant or automation from pilot to production. Includes concrete evidence examples, rating guidance, sample mitigations, and suggested production thresholds.
A practical dashboard specification and playbook to observe agent usage, detect reliability and safety issues early, run rolling human audits, measure provenance coverage, record sensitive-data attempts, and trigger remediation workflows with clear ownership.
A practical, reusable template to document a model's purpose, scope, inputs, outputs, data lineage, architecture, performance, known limitations and failure modes, observed biases, interpretability techniques, guardrails, monitoring and remediation plans, human-in-the-loop checkpoints, vendor and privacy notes, and audit history. Includes a completed example for a simple summarization model used in knowledge search.