Quick Answer
Build a custom GPT for video prompts with a reusable schema, test cases, and current OpenAI privacy and security guidance for training teams.
Quick answer
Create a custom GPT with explicit instructions, a reusable output schema, approved reference files, and test cases from your real training workflow. Require it to separate factual source content from creative visual direction and to flag missing information. Keep sensitive material out until your organization has approved the account, sharing, retention, training, and any third-party app or API settings.
What this GPT should, and should not, do
A custom GPT for video prompts can standardize how instructional designers turn a lesson brief into prompts for document-to-video, text-to-video, or image-to-video systems. It can ask for missing context, apply brand vocabulary, and return prompts in a predictable format.
It should not become the authority for safety, policy, legal, medical, or technical content. The source owner remains responsible for facts, and a human remains responsible for the final prompt and generated scene.
Define a narrow job:
Convert approved training briefs into production-ready AI video prompt packages. Preserve source meaning, never invent instructional facts, and separate narration, visuals, motion, and review notes.
That is more testable than “help our team make great videos.”
What OpenAI currently provides
OpenAI describes GPTs as configured versions of ChatGPT that can combine:
- Instructions for behavior, goals, tone, and boundaries;
- Knowledge files used as reference;
- Capabilities such as web search or image generation;
- Apps or actions that can connect to external services;
- Conversation starters that show users how to begin.
According to OpenAI’s current help documentation, creating or editing a GPT requires a paid subscription and may also depend on managed-workspace roles and settings. GPTs can be built conversationally or configured in the editor, tested in Preview, saved, and shared according to available permissions. Product labels and availability change, so confirm the current help pages when implementing.
OpenAI also states that GPTs do not use saved memory, account custom instructions, or previous conversations; each conversation starts fresh. That makes durable GPT instructions and a complete per-project brief important.
Step 1: Decide the inputs
Ask users for only the information the GPT needs:
Project name:
Audience and prior knowledge:
Learning objective:
Approved source or script:
Video mode: document-to-video / text-to-video / image-to-video
Target platform or model, if known:
Aspect ratio and target duration:
Visual format and brand style:
Required points:
Prohibited or unverified content:
Accessibility requirements:
If a user omits the objective, source, or mode, the GPT should ask a question rather than guess. If the tool or model is unknown, it should produce a neutral base prompt and advise the user to adapt it to that model’s current documentation.
Step 2: Create the GPT
In ChatGPT, open the GPT creation area available to your plan and workspace, then choose Create. OpenAI’s help center says you can build conversationally or configure fields directly. For a team workflow, direct configuration makes rules easier to audit.
Set:
- a specific name, such as Training Video Prompt Builder;
- a description that states the intended users and limits;
- conversation starters based on common tasks;
- instructions containing the operating rules below;
- only the knowledge files and capabilities you have approved.
Useful starters:
- “Turn this approved lesson brief into a document-to-video prompt package.”
- “Write three text-to-video prompts for this storyboard.”
- “Adapt this shot for image-to-video; focus on motion.”
- “Audit these prompts for unsupported instructional claims.”
Test in Preview before sharing, as OpenAI recommends.
Step 3: Add operating instructions
Paste and adapt this instruction block:
ROLE
You are the Training Video Prompt Builder. You help instructional designers
write prompts; you do not approve instructional content.
SOURCE DISCIPLINE
1. Treat user-identified approved sources as factual authority.
2. Never invent procedures, policy, technical values, legal requirements,
statistics, quotations, or learner outcomes.
3. Distinguish source facts from creative visual suggestions.
4. If sources conflict or a required fact is missing, stop and list the issue.
5. Use placeholders for exact interfaces, labels, PPE, signs, and equipment
unless verified reference media is supplied.
WORKFLOW
1. Identify video mode: document-to-video, text-to-video, or image-to-video.
2. Check for audience, objective, approved source, required points, duration,
aspect ratio, visual approach, and constraints.
3. Ask up to three focused questions if essential information is missing.
4. Produce the output using the required schema.
5. End with a human-review checklist.
PROMPTING RULES
- For text-to-video, describe visible subject, action, environment, framing,
motion, and relevant style in direct positive language.
- For image-to-video, do not redescribe established image details unless needed;
focus on subject, scene, and camera motion over time.
- Keep each generative shot to one coherent visual beat unless the target
model's documentation supports more.
- Do not claim that a prompt guarantees exact duration, continuity, or accuracy.
ACCESSIBILITY
- Do not rely on colour alone.
- Flag visual-only instructional information for narration, audio description,
or an equivalent alternative.
- Keep on-screen text concise and mark exact approved wording.
OUTPUT
Use the Prompt Package Schema exactly. Label assumptions and unresolved risks.
This structure reflects general principles in current Adobe and Runway prompting guidance: text-to-video needs visual and motion descriptions, while image-to-video prompts should concentrate on motion already grounded by the input image.
Step 4: Use a reusable prompt schema
Require the same output every time:
project_summary:
audience:
learning_objective:
source_of_truth:
video_mode:
target_runtime:
aspect_ratio:
content_guardrails:
required_points: []
prohibited_or_unverified: []
assumptions: []
questions_for_reviewer: []
prompt_package:
narration_or_message:
master_visual_direction:
shots:
- shot_id:
purpose:
factual_basis:
prompt:
on_screen_text:
target_duration:
continuity_notes:
accessibility_notes:
human_review:
factual_checks: []
visual_checks: []
privacy_or_rights_checks: []
YAML is useful because it is readable and consistent, but plain Markdown headings work if your team finds them easier. The value is the explicit separation of evidence, creative direction, and review.
Step 5: Add knowledge carefully
Suitable knowledge files may include:
- a public or approved brand and tone guide;
- an instructional design checklist;
- a controlled vocabulary or pronunciation guide;
- approved prompt examples;
- accessibility standards interpreted by your organization;
- a list of prohibited depictions or claims.
Do not upload an entire confidential repository simply for convenience. Curate the smallest useful set, remove personal data where possible, assign an owner, and establish a review date. A GPT’s knowledge is a reference library, not a records-management system.
OpenAI’s File Uploads FAQ says files uploaded as knowledge are retained until the custom GPT is deleted; after deletion, associated files are deleted from OpenAI systems within 30 days, subject to stated exceptions such as legal or security obligations or prior de-identification. Check the current policy and your contract before relying on that summary.
Step 6: Set security and privacy boundaries
OpenAI says data use depends on the plan. Its GPT guidance states that Business, Enterprise, and Edu data is not used for training by default; on consumer plans, use may depend on Data Controls. The Data Controls FAQ explains how signed-in users can turn off “Improve the model for everyone.”
Plan-level defaults do not replace organizational approval. Before use, determine:
- which account and workspace are authorized;
- whether prompts may contain confidential or personal data;
- who can edit, use, or share the GPT;
- how knowledge files will be reviewed and removed;
- whether conversation and file retention meets policy;
- whether outputs can leave the workspace.
OpenAI says GPT builders cannot view individual user conversations. It also warns that when a GPT uses apps or external APIs, relevant input may be sent to the third party and OpenAI does not control how that service stores or uses the data. Disable unneeded capabilities. Approve each external service separately and never assume the GPT’s main workspace protections automatically extend to it.
Prompt injection is another practical concern. A source file or pasted webpage can contain instructions that try to override your rules. Tell users to treat retrieved content as data, restrict web access if unnecessary, and require human approval before any external action or publication.
Step 7: Test before rollout
Build a small evaluation set:
- a complete, low-risk lesson brief;
- a brief missing its learning objective;
- conflicting source documents;
- a request to invent a compliance statistic;
- an image-to-video task where the image already supplies style;
- a prompt containing personal or confidential data;
- an ambiguous equipment demonstration.
Score each output for source fidelity, useful questions, schema compliance, visual clarity, accessibility flags, and refusal to invent. Retest after changing instructions or knowledge files. Keep version notes and name an owner who can withdraw the GPT if it behaves unexpectedly.
For guidance on project briefs, see Do AI Video Generators Need Context?. For selecting the right workflow, compare AI video tools for training and education.
FAQ
Do I need coding skills to build a custom GPT?
No for a basic configured GPT. OpenAI provides an editor for instructions, knowledge, capabilities, and starters. Actions that connect external APIs require additional technical and security work.
Can the GPT learn from our previous prompt conversations?
OpenAI currently says GPTs do not use saved memory or previous conversations. Put durable rules in configuration and provide project context in each conversation.
Should we upload our full training library?
Usually not. Start with a curated, approved set of reusable guidance. Minimize sensitive data and confirm plan, retention, sharing, and governance requirements first.
Can GPT builders read employees’ chats?
OpenAI says builders cannot view individual conversations users have with their GPTs. Workspace administrators and connected third parties may be subject to different controls, so review the relevant organizational terms.
Will this GPT work with every AI video generator?
It can create a neutral prompt package, but model syntax, controls, and limits differ. Maintain model-specific adapters based on current first-party documentation.