A practical guide to building with generative AI

Generative AI can draft, summarise, retrieve, classify, and transform content. It can also produce plausible errors, repeat source problems, or act through an over-permissioned tool.

Our eBook, Innovate with GenAI, was published in 2024 to help teams frame an Azure AI initiative. Product names, model availability, and interfaces can change, so use current Microsoft documentation for implementation.

Start with one user task

Name the user, input, output, and decision the system supports. Capture the current time, quality, cost, and risk so the pilot has a baseline.

Good first tasks have a clear review path and a bounded source set. Avoid a general assistant whose success cannot be measured.

Choose the deployment path from requirements

Model choice follows the task, data location, latency, safety, and cost requirements. Confirm the model, region, feature, and support status in the current Azure catalogue.

Microsoft’s Foundry availability guidance separates generally available and preview capabilities. Do not treat the status of one service component as the status of the whole solution.

Ground and evaluate the output

Retrieval can give a model approved business context, but it does not guarantee a correct answer. Test source selection, citations, freshness, conflicts, and behaviour when evidence is missing.

Create an evaluation set from real user tasks. Include ordinary cases, edge cases, adversarial prompts, and users with different permissions. Measure factual support and task completion, not only writing quality.

Protect data and actions

Map which data enters prompts, indexes, logs, and evaluations. Apply least privilege to source systems and tools. Keep a person in control of actions that affect money, access, safety, employment, or legal rights.

Document retention, incident response, model changes, and how the feature can be disabled. Those controls make experimentation reversible.

What the eBook covers

The guide introduces:

• business use-case selection
• Azure deployment choices
• retrieval and application patterns
• data safeguards and responsible AI
• operating and improvement practices

It also contains examples and product references from its 2024 publication date. Verify those details before reusing them in a current architecture.

The original download route now redirects to this article, and no local eBook file was available during this update. The article preserves its planning themes, not a claim that the download is still active.