Your subject matter experts know the domain better than anyone on the engineering team. They understand the edge cases, the tone customers expect, and the difference between a good AI output and a mediocre one. But when it comes to actually changing the prompt behind your AI feature, they are locked out.

The prompt lives in code. Code lives in Git. Git requires a pull request. The pull request requires an engineer. And the engineer has a sprint backlog. So the domain expert writes their suggested change in a Google Doc, pings someone on Slack, and waits. Sometimes for days.

This bottleneck is not just annoying. It slows down your AI product’s ability to improve. And it means the people with the deepest knowledge of what makes a good output are the furthest from the controls.

This article is for product managers, domain experts, and team leads who work on AI features but don’t write code. We’ll walk through why you should be involved in prompt management, what you need from a prompt management system, and how to set up a workflow where the whole team can contribute to prompt quality without breaking anything in production.

Why Non-Engineers Should Be Involved in Prompt Work

Prompts are not code. They are instructions written in plain language that shape how an AI model behaves. Product managers write PRDs. Customer support leads write scripts. Marketing teams write copy. Writing prompts is closer to these skills than to software engineering.

The McKinsey State of AI 2025 survey found that nearly two-thirds of organizations have not yet begun scaling AI across the enterprise, with most stuck in experimentation or piloting. One major reason? Teams fail to redesign workflows to include the right people. When domain experts stay on the sidelines, AI features stall.

A Harvard Business School study conducted with Boston Consulting Group showed that knowledge workers using AI tools produced results that were 40% higher in quality compared to those who did not. The key factor was not technical skill. It was familiarity with the subject matter and the ability to iterate on instructions (prompts) rapidly.

Consider a healthcare company building an AI triage assistant. The compliance team knows which phrases are medically accurate. The support team knows which tone reduces patient anxiety. Neither team has Git access. If they cannot edit prompts directly, the engineering team becomes a translation layer, and meaning gets lost in translation.

The people closest to the problem should be closest to the prompt.

The Broken Handoff: How Teams Work Around It Today

Most teams have developed informal workarounds for this bottleneck. None of them work well.

The spreadsheet method. A subject matter expert tests prompt variations in a Google Sheet or a Jupyter Notebook. They copy outputs, compare them manually, and eventually share a “winning” prompt with the engineering team via Slack or email. The engineer copy-pastes it into the codebase. Neither side has full context about what the other tested or why.

The ticket-and-wait method. The product manager files a Jira ticket describing the prompt change they want. An engineer picks it up, makes the change, and deploys it. There is no easy way for the PM to test the change before it goes live. If the output is not right, the cycle starts again.

The pair-programming method. An engineer and a domain expert sit together and iterate on prompts in real time. This produces good results but does not scale. It requires both people’s calendars to align, and there is no record of what was tried or why specific changes were made.

All three methods share the same problems:

• There is no shared history of what was tried

• There is no way to compare prompt versions systematically

• There is no safe path from experiment to production

• Learning stays locked inside one person’s head instead of becoming team knowledge

As one AI team lead put it in a discussion about prompt versioning: “There’s no shared learning company-wide, no way for product teams to take initiative. If they do take initiative, they can’t bring things to production safely.”

What Non-Engineers Need from a Prompt Management System

Not every tool will solve this. A prompt management platform for cross-functional teams needs specific capabilities. Here is what to look for.

A Visual Playground That Requires No Code

The starting point is a browser-based interface where anyone can edit a prompt template, fill in test inputs, and see the model’s output. No terminal. No IDE. No Git commands.

A good playground uses template variables (like {{customer_name}} or {{product_description}}) so that anyone can understand the prompt structure at a glance. The Agenta playground, for example, supports both simple curly-bracket variables and Jinja2 templating for more complex logic. Variables are automatically detected and turned into input fields.

Side-by-Side Comparison Mode

Editing a prompt and reading a single output tells you little. You need to compare two versions of a prompt across the same set of test inputs. Did Version B handle the edge case better? Did it introduce a regression in the common case?

A comparison mode puts two prompt variants in separate columns. You enter the same input, run both, and see how the outputs differ. This is how product managers and domain experts can do real prompt engineering without writing code.

Test Sets You Can Load and Reuse

Ad hoc testing (trying one input at a time) catches obvious issues but misses patterns. You need a set of representative test cases that you can run against every prompt change.

Test sets are typically CSV files with one row per test case. Each column maps to a template variable. Upload once, reuse across sessions. When you change a prompt, load the test set and run all cases at once to check for regressions.

Safe Environments for Experimentation

You should be able to experiment freely without any risk of affecting real users. This means the system should separate experimentation from production through environments (like development, staging, and production) and through variants (like branches in Git).

In Agenta, variants work like branches. Each variant has its own history. You can create a new variant, try changes, and if the results are good, deploy that variant to staging. If not, discard it. Production stays untouched throughout.

Human Evaluation Built In

Automated metrics catch some issues, but for many use cases (tone, accuracy, helpfulness) human judgment is required. The system should let you set up human evaluation workflows where reviewers score outputs against criteria you define. This turns informal “looks good to me” assessments into structured, comparable data.

Role-Based Access Controls

Not everyone should deploy to production. A good system lets you configure who can edit prompts, who can run evaluations, who can deploy to staging, and who can push to production. This gives non-engineers the freedom to experiment while keeping production safe.

How to Set Up a Workflow That Includes the Whole Team

Having the right tool is half the picture. You also need a process. Here is a step-by-step workflow that lets product managers, domain experts, and engineers collaborate on prompts without stepping on each other’s toes.

1. Define who owns what. Assign clear roles. For example: domain experts own prompt content and test cases. Engineers own the integration and deployment pipeline. Product managers own the evaluation criteria and prioritization. Document this in your team wiki.

2. Build a shared test set. Before anyone starts changing prompts, create a test set that covers your most important cases. Include common inputs, edge cases, and known failure modes. This becomes the team’s quality baseline. Anyone proposing a prompt change should run it against this test set.

3. Use variants for experimentation. When a domain expert wants to try a different approach, they create a new variant in the playground. They make changes, run the test set, and compare outputs against the current production variant. No code changes, no deploys, no risk.

4. Run structured evaluations. Instead of asking “does this look good?”, set up evaluations with specific criteria. For a customer support bot, you might score on accuracy, tone, and completeness. For a medical triage tool, you might score on clinical correctness and patient safety. Use human evaluation for subjective criteria and automated evaluators for measurable ones.

5. Review as a team before deploying. When a variant looks promising, the team reviews it together. The domain expert explains the changes. The PM reviews the evaluation scores. The engineer checks for any integration concerns. Then the engineer deploys the approved version to staging.

6. Test in staging, then promote to production. Run the staging version against real-world scenarios (or a shadow of production traffic if possible). If it holds up, deploy to production. If not, iterate in the playground and repeat.

7. Document what you learned. After each change, note what was tried, what worked, and what didn’t. This creates a shared knowledge base that prevents the team from re-trying failed approaches and helps onboard new team members.

Getting Started with Agenta

Agenta is an open-source LLMOps platform built for this kind of collaboration. It gives non-engineers a browser-based playground to write, test, and compare prompts without touching code. And it gives engineers the version control, environment management, and deployment controls they need to keep production safe.

Here is what makes it a fit for cross-functional prompt management:

• Playground with template variables. Write prompts with {{variable}} placeholders. Agenta detects them automatically and creates input fields. No code needed.

• Comparison mode. Put two prompt variants side by side. Run the same inputs through both. See how outputs differ across every test case.

• Variants and environments. Create branches (variants) for experiments. Deploy to dev, staging, or production when ready. Roll back if needed.

• Test set support. Upload CSV test sets and run them against any variant. Check for regressions before you deploy.

• Human evaluation. Set up structured evaluation workflows where reviewers score outputs on criteria you define (accuracy, tone, completeness). Collect annotations to improve prompts over time.

• Role-based access. Control who can edit, evaluate, and deploy. Give domain experts editing access without giving them the production deploy button.

You can start for free on Agenta Cloud or self-host the open-source version.

FAQ

Can product managers edit prompts without knowing how to code?

Yes. A prompt management platform with a visual playground lets you edit prompt templates, fill in test inputs, and see outputs in a browser. You write the prompt in plain language using template variables like {{customer_name}}. No terminal, IDE, or Git access required. The key is choosing a system that separates prompt editing from code deployment.

How do you prevent non-engineers from breaking production prompts?

Through environments and role-based access. A good prompt management system separates development, staging, and production. Non-engineers can experiment freely in development using variants. Only authorized team members (typically engineers or tech leads) can deploy changes to production. This gives everyone the freedom to iterate while keeping production safe.

What is the difference between a prompt playground and ChatGPT?

ChatGPT is designed for one-off conversations. A prompt playground is designed for systematic prompt development. In a playground, you work with template variables, compare multiple prompt versions side by side, load test sets with dozens or hundreds of test cases, and track version history. It is a development environment for prompts, not a chatbot.

How do you get engineers to trust non-engineers with prompt changes?

Start with a shared test set and a structured evaluation process. When domain experts can demonstrate that their changes improve scores across a representative set of test cases, engineers gain confidence in the process. Environment separation also helps; non-engineers experiment in development, and engineers retain control over what reaches production. The goal is not to remove engineers from the loop. It is to remove them from the bottleneck.