How to Run a DIY AI Red Team: Checklist & Test Cases
Step-by-step playbook to run a lightweight internal AI red team: scope, prioritized test cases, ready-to-run prompts, severity scoring, reporting, and remediation.
Design a Reliable AI Agent Workflow for Small Teams
Step-by-step playbook for small teams to design dependable AI agent workflows: define scope, choose managed vs hosted, add human checkpoints, logging, safety, and rollout.
How to Evaluate AI Content Tools: Reproducible Rubric & Tests
A practical, reproducible rubric and small test suite to evaluate AI content tools. Includes prompts, scoring, automation recipes, decision matrix, and limits.
Manage LLM Costs in Production: A Step-by-Step Playbook
A practical playbook to manage LLM costs in production: choose models wisely, cut tokens, batch and cache calls, use retrieval over generation, and estimate spend.
Build an AI Moderation Workflow for UGC: Policies & Steps
Step-by-step guide to build an AI moderation workflow for UGC: policy tiers, triage rules, AI filters, human review, testing checklist, and vendor checklist.
Reduce Hallucinations in RAG: Practical Checklist & Tests
A hands-on checklist to reduce hallucinations in retrieval-augmented generation (RAG), with concrete prevention steps, reproducible tests, code/no-code examples, and onboarding tips.
Model‑Agnostic Architecture: Swap LLM Providers with Ease
A practical guide to designing model‑agnostic app architecture: adapter layers, unified prompts, normalization, feature flags, evaluation harnesses, and checklists.
Gemini Notebook Workflow: A Practical Guide for Class Projects
Gemini Notebook workflow for students: ingest PDFs, slides, and webpages to make citation-aware summaries, clear outlines, and exportable project reports.
What Is Full‑Stack AI? Layers, Roles, and Where to Start
A practical guide to full‑stack AI: the core layers, who owns them, vendor choices, tradeoffs, and checklists to run safe, low-risk AI experiments.
AI privacy checklist: 10 checks to test an AI tool in an hour
A non-technical, one-hour AI privacy checklist with 10 concrete tests, vendor questions, sample prompts, and a printable team takeaway to verify data safety.