Guide

AI agents can be useful because they do more than answer questions. They can plan steps, use tools, retrieve data, update systems, send messages, and trigger workflows. That is also why teams need a governance checklist before agents move into real work. Quick Answer Create an AI agent governance checklist by defining the agent owner, workflow scope, allowed tools, allowed data, permission limits, review points, logs, escalation rules, cost limits, and incident response process. ...

Building a generative AI app in Azure is not only about deploying a model. A useful app needs the right project setup, a suitable model, secure service connections, grounding data, safety controls, and a repeatable evaluation process. This guide turns the AI-102 training material on Microsoft Foundry into a practical build path for teams that want to plan, develop, and evaluate generative AI applications on Azure. Quick Answer To build a generative AI app in Azure with Microsoft Foundry, create a Foundry project, choose and deploy a model from the model catalog, connect to the project with the Microsoft Foundry SDK, build a chat or RAG workflow, add safety controls, and evaluate the app before broad release. ...

An AI prompt library helps teams reuse prompts that actually work. Without one, every person writes their own instructions, quality varies, and useful improvements disappear into private chats. Quick Answer Set up an AI prompt library by choosing high-value workflows, writing reusable prompt templates, adding examples, assigning owners, tracking versions, and reviewing prompts when tools, policies, or workflows change. Key Takeaways Store prompts by workflow, not by tool alone. Include examples and quality checks with every prompt. Assign an owner so prompts do not become stale. Version prompts when the wording changes. Remove prompts that are rarely used or often corrected. Step 1: Choose the First Workflows Start with repeated work such as: ...

AI can create useful drafts quickly, but publishing without review can create factual, brand, privacy, and trust problems. A clear review workflow helps teams use AI without handing over final judgment. Quick Answer Review AI outputs by checking accuracy, sources, sensitive data, audience fit, brand voice, formatting, and final human approval before publishing or sending anything customer-facing. Key Takeaways Review facts before polishing style. Check whether the output includes sensitive or restricted data. Ask for source support when the content makes factual claims. Keep a human owner responsible for the final version. Use stricter review for public, legal, financial, HR, or customer-impacting content. Step 1: Identify the Output Type Different outputs need different review levels. ...

Better AI research prompts do not simply ask for information. They define the decision, the audience, the evidence standard, and the output format. That is what turns a broad AI answer into useful research notes. If you use ChatGPT, Claude, Gemini, Perplexity, or another AI assistant for research, the prompt should make uncertainty visible. The goal is not just a confident answer. The goal is an answer you can check, compare, and use. ...

Before a team adopts an AI tool, privacy should be checked in plain language. The goal is not to slow down adoption. The goal is to avoid sending sensitive data into tools that are not designed for that risk. Quick Answer To evaluate AI tool privacy, identify the data type, review training and retention policies, check admin controls, test with low-risk data, and document what the tool is approved to handle. ...

Small teams can get real value from AI tools, but too many tools quickly create cost, confusion, and security risk. The best stack is small, practical, and connected to workflows your team already repeats. Quick Answer To build an AI tool stack for a small team, start with one general assistant, add specialist tools only for repeated workflows, define data rules, run a short pilot, and remove tools that are not used after 30 days. ...

Choosing the right AI model is a balance between quality, speed, cost, context length, and risk. The best model is not always the most powerful one. It is the one that fits the task. Quick Answer Choose a stronger model for complex reasoning, long documents, coding, analysis, and high-value decisions. Choose a faster or cheaper model for simple rewriting, classification, extraction, and routine drafts. Key Takeaways Match model strength to task difficulty. Do not use the most expensive model for every task. Long context matters when the model must read large documents. Reliability matters more for customer-facing, legal, financial, or technical work. Test models on real examples before standardizing. Step 1: Classify the Task Group tasks by complexity: ...

ChatGPT can speed up content production, but the best results come from a structured workflow. Treat it like a writing partner, not a publish button. Quick Answer Use ChatGPT for content writing by starting with a detailed brief, generating outlines first, drafting section by section, adding real examples, editing for accuracy and brand voice, and creating supporting SEO assets before publishing. Key Takeaways The brief matters more than the first prompt. Outlines give you control before drafting begins. Section-by-section writing usually creates better content. Real examples make AI output less generic. Human editing is still essential for trust. Step 1: Write a Strong Brief Before asking for a draft, define: ...

Choosing an AI tool is easier when you use a clear decision framework instead of chasing hype. The right tool should improve a real workflow, fit your data rules, and be easy enough for the team to use consistently. Quick Answer To choose the right AI tool, define the workflow, set non-negotiables, compare a small shortlist, run a real pilot, measure results, and document when the tool should or should not be used. ...