Prompt & AI Workflow Enablement

Hands-On Prompt Engineering Workshop

Prompt engineering becomes reusable when participants start from a real task, tie each revision to an observed defect, and keep test cases, data boundaries, and ownership with the pattern.

Participants bring work they know and the criteria its output must meet. In the lab they break down the task, compare prompt choices on representative and difficult cases, verify defects, and record the patterns that deserve another test.

Participants leave with task breakdowns, prompt-practice sheets, reviewed samples, and approved patterns whose limits, verification steps, and next-workflow owners are written down.

Plan the prompt labSee corporate AI training
Illustration of Hands-On Prompt Engineering Workshop: a team testing and refining prompts against real examples

Some of the 500+ brands we've worked with

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  • Mini
  • PWC Türkiye
  • Marks & Spencer
  • Memorial
  • Onedio
  • Country Floors
  • Evreka

Steps, gates, and who decides

How we work

A prompt is useful here only as part of a task the group can test, inspect, and revise. Every change answers an observed failure, and every shared pattern keeps its cases, data boundary, and owner.

  1. The task comes first

    Participants define the job, intended user, available context, constraints, examples, quality criteria, and data boundary before writing the prompt.

    AI assist
    The approved tool can draft a task breakdown from the participant's example for them to correct.
    Human gate
    Is the task specific enough to test with representative cases? The participant confirms that the breakdown matches the job they actually do.

  2. Variants answer observed failures

    We build and compare prompt variants, structured outputs, examples, and tool settings while the facilitator exposes hidden assumptions.

    AI assist
    The approved model generates prompt variants for the group to compare side by side.
    Human gate
    Which observed failure is this change meant to address? The facilitator decides which variant actually targets an observed failure.

  3. Difficult cases expose the defects

    Participants run representative and difficult cases, verify the outputs, classify defects, and revise the prompt or workflow where evidence supports it.

    AI assist
    The approved tool runs the test cases and flags outputs that resemble defects.
    Human gate
    Does the pattern hold up beyond the easiest example? A participant verifies whether a flagged output is a genuine defect.

  4. Patterns leave with limits and owners

    The group documents patterns, limits, test cases, and next-workflow commitments so useful work can continue after the lab.

    AI assist
    A model drafts the first pattern write-up from the prompts reviewed in the session for the owner to edit.
    Human gate
    Who will maintain the pattern and its test cases? The person who owns this pattern accepts it before it's published for reuse.

Named artifacts you keep

What you get

The record keeps the experiment behind the wording. It includes the task breakdown, test cases, defect evidence, reviewed samples, known limits, and ownership for the next approved workflow.

  • Workshop record

    Facilitated prompt-lab agenda and exercise plan

    The agenda, role groups, tools, data boundaries, target tasks, exercises, and review points.

  • Curriculum

    Task-decomposition and prompt-practice sheet

    Guided practice for task decomposition, context, constraints, examples, output structure, and verification.

  • Test evidence

    Representative-case rubric and reviewed-sample file

    Representative cases, quality criteria, defect categories, and examples reviewed during the lab.

  • Playbook

    Approved prompt patterns, limits, and owner list

    Reviewed patterns with intended use, limits, verification steps, and owners for the next workflow.

Scope and honest limits

When to bring us in

The group already knows how to write a prompt. The gap appears when a polished example meets a difficult case and nobody can explain what to change or why.

A good fit when

  • A prompt circulates because it sounds polished, but nobody can name the test cases or quality bar used to judge its behavior.
  • Participants can write instructions, yet they cannot choose which context, constraints, examples, or output structure the real task needs.
  • Teams want reusable prompt patterns, but approved models, data boundaries, and verification steps are not attached when those patterns are shared.
  • A real task reaches the workshop as one block, so prompt revisions chase wording before context, constraints, examples, and outputs are separated.
  • The same failures return across variants, while nobody records which observed defect each prompt change was meant to repair.
  • Test cases and structured outputs exist, but participants compare them without one rubric or a person confirming which flags are real defects.
  • Useful prompt patterns leave the lab, yet their known limits, test cases, next-workflow commitments, and owners are not recorded.

Better handled as other work when

  • You need sensitive material used in an unapproved tool. The workshop stays inside approved data rules, while tool authorization belongs to the policy owner.
  • You want prompt wording accepted without checking behavior. The lab runs representative and difficult cases, while participants verify the defects.
  • You need production prompt infrastructure or integrations built during the workshop. The lab ends with reviewed patterns, and engineering needs a separate scope.

If one of these is closer to your situation, start here instead: See corporate AI training

Your trainers build AI for a living

The people who run our training build and operate AI systems the rest of the week, so the material comes from work we've shipped. Zeo has been around since 2011 and runs the Digitalzone conference community, which keeps us close to how teams across the industry are picking these tools up.

Content we've produced on this topic

Articleby Can MutioğluWhat is the Few-Shot Prompting Technique? How to Use It?Articleby Ezgi Gülsen YaylıGuidance with Chain of Thought in Artificial IntelligenceVideoby Ozan KetenciA Hands-On Guide to Elevating Efficiency with OpenAI's Custom GPT - Ozan Ketenci, ZeoVideoby Yiğit KonurAI's Future: Mastering Prompt Engineering & AI Integration - @Yiğit Konur , Wope

Tools we use

Tools behind this work

  • OpenAI

    one of the live models participants test prompt variants against directly

  • Anthropic

    the second live model, testing whether a pattern transfers across providers

  • PromptLayer

    versions every variant tested, tracing an improvement to a specific edit

  • Braintrust

    scores which patterns hold up, with limits recorded, the workshop's real output

  • Jupyter

    runs difficult-case testing as inspectable code, so defects can be reproduced

Next step

Test prompts on work your team knows

The lab begins with a task your team knows, the approved tools, its data boundary, and a quality bar. Our facilitator uses those inputs to choose the cases that can test the prompt and show whether it deserves reuse.
Discuss the prompt lab

Before you decide

Questions we hear a lot

Wording is one part of it. The result also depends on the task definition, context, constraints, examples, output structure, tool settings, verification, and the surrounding workflow. We judge what the prompt does on the agreed cases, including where it fails.