Best Practices

AI lab generation works best when you give the assistant a clear goal, good context, and steady feedback. These practices will help you get labs that are accurate, on-brand, and close to what you want on the first pass.

Start with strong research

Generation is only as good as the research behind it. Before you generate:

Publish accurate company research. This is what grounds the lab in your business, terminology, and brand style.
Add the relevant products. Selecting products focuses the lab on the real subject matter.
Fix research first, not the lab. If generated content misrepresents your product, correct the underlying research and it will improve every future lab, not just this one.

Write a clear prompt

The prompt is your main instruction. Aim for a specific, outcome-focused description:

State the learning goal. Describe what a learner should be able to do by the end, not just the topic. “Teach a developer to deploy a service and roll back a bad release” beats “a lab about deployments”.
Name the scenario. Mention the tools, product, or workflow the lab should center on.
Add any hard requirements. Call out things the assistant should include or avoid — specific commands, a particular flow, concepts to skip.
Keep it focused. One coherent objective per lab produces a tighter result than a prompt that tries to cover everything.

Choose settings that match your audience

Audience experience shapes depth and pacing. Pick Beginner for step-by-step fundamentals, Advanced when you can assume prior knowledge and move faster.
Duration guides scope. A 10-minute lab is a focused exercise; a 2-hour lab is a deeper, multi-chapter journey. Match it to how much you actually want to cover.
Autonomy sets the style. Lean Guided for precise, follow-along instructions; lean Autonomous when you want learners to explore and problem-solve.

Review as you go

The plan-first, chapter-by-chapter flow exists so you can steer early — use it:

Review the roadmap before approving. It’s far cheaper to fix the shape of the lab at the plan stage than after chapters are built.
Check the first chapter carefully. It sets patterns the rest of the lab follows. Getting it right early prevents issues from compounding.
Give specific feedback in chat. When something’s off, say exactly what you want changed rather than regenerating and hoping. Precise guidance (“use our CLI instead of the web console”) produces better corrections.
Answer the assistant’s questions directly. Clear answers to clarifying questions keep the generation on track.

Iterate after generation

A generated lab is a strong starting point, not a final artifact:

Re-open the assistant to make conversational edits as your product or training needs change — no need to regenerate.
Make precise tweaks yourself in the Files view or the standard lab editor when that’s faster than describing them.
Always review before publishing. Treat generated content with the same care as hand-authored content — read it through and validate it before it reaches learners.