How to Evaluate an AI Coding Assistant When You’re Not a Developer
A practical checklist for non-developers to evaluate AI coding assistants: usability, privacy, integrations, offline options, hallucination risks, and cost trade-offs.
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Secure prompt management: store, version, and audit prompts
A practical tutorial on storing, versioning, and auditing prompts safely: secret stores, redaction, git-backed templates, access controls, and audit patterns.
Build an AI Customer Support Bot with Limited Data: Step-by-Step Guide
A practical, low-data workflow to build an AI customer support bot: define scope, prepare small datasets, use RAG-lite or lightweight models, test guardrails, deploy.
How to Build an AI-Powered Meeting Summaries Workflow That Works
Step-by-step guide to build a reliable AI meeting summaries workflow: choose tools, record and transcribe, integrate notes, craft prompts, QA results, and automate delivery.
Build a Safe AI Research Assistant That Cites Sources — Step-by-Step
Step-by-step guide to building a reliable AI research assistant that returns sourced answers, verifies claims automatically, and flags items for human review.
How to Test and Prevent LLM File-Access and Deletion Risks
A hands-on playbook for teams to simulate risky LLM behavior, lock down file permissions, run safe tests, monitor activity, and prepare incident playbooks.
Practical framework to audit an AI tool’s data handling
A compact, step-by-step 12‑point checklist to audit an AI tool’s data handling—collection, retention, sharing, training risk, vendor contracts, access controls, and tests.
AI Code Security Evaluation Checklist for Engineering Managers
A practical checklist for engineering managers to evaluate AI code tools: data handling, sandboxing, telemetry, training claims, logging, incident response and vendor questions.
Text-to-Video Tools Compared: Workflow for 30–60s Ads
Compare six text-to-video tools and follow a step-by-step workflow to produce 30–60s marketing videos: scripts, prompt templates, shot breakdown, captions, exports.
How to Set Token Budgets and Control Costs with LLM APIs
Practical step-by-step guide to define token budgets, monitor LLM usage, implement throttles and fallbacks, and set alert thresholds so teams can control API costs.