Steve Albanese - AI Solutions Consultant - KnowledgeMiner

In the fast-paced world of media production, efficiency and consistency are key. As teams juggle multiple tasks—from visual design to scripting and location creation—ensuring everyone works from the same set of details can be a challenge. This is where a custom GPT, specifically tailored to handle production complexities, comes in. The key to unlocking its full potential lies in how you customize its interaction with users—through carefully written instruction sets.

The Power of Instructions: Defining GPT’s Role in Production

The most crucial step in creating a custom GPT is writing the instructional text that governs how it interacts with users. Rather than just inputting a general knowledge base, you need to provide the GPT with explicit instructions to guide its behavior in practical production settings. These instructions act as the blueprint for GPT interactions, helping it respond appropriately to your team's needs.

Crafting Instructional Documents: A Step-by-Step Guide

1 - Understand the Workflow: Start by breaking down the production process into key tasks and interactions between the GPT and users. These might include tasks like storyboard creation, character design, script support, or location scouting. Each task should have a specific set of instructions the GPT can follow to guide users step by step.

2 - Write the Instructions: Once you’ve outlined the workflow, write clear, interactive instructions that will trigger the GPT to ask specific questions and prompt the user to provide answers. For example, when a user requests to create storyboard frames, the GPT will initiate an interactive dialogue by asking a series of questions to gather necessary details. This ensures that the GPT gathers all the context it needs to generate a useful output. Here’s how that interaction might flow:

• User request: "Create storyboard frames for the next scene."
• GPT response: "Great! Let’s start by setting the style. What visual style would you like for these frames? (e.g., cartoon, anime, hyper-realistic)" User answer: "Hyper-realistic."
• GPT follow-up: "Which characters are involved in this scene?" User answer: "Butch and Guns"
• GPT follow-up: "How do Butch and Guns feel in this scene? (e.g., happy, angry, confused)" User answer: "Butch is angry, and Guns is confused."
• GPT follow-up: "What is the location for this scene?" User answer: "They’re in a night club."
• GPT follow-up: "What are Butch and Guns doing or what body positions are they in?" User answer: "Butch is standing in the crowd with his arms flexed, and Guns is standing in the crowd flexed with his Entourage, looking with tension at Butch."
• GPT summary: "Got it! Hyper-realistic frame with Butch (angry, Butch is standing arms flexed in the crowd) and Guns (confused, standing, flexed ) in the night club crowd. Is that correct?"
• User confirmation: "Yes."

3 - Compile and Retrieve: Once the user has answered all the questions, the GPT compiles the necessary information. It then retrieves the relevant details from the knowledge base—such as the character descriptions and location data—ensuring the final output is accurate and aligned with the project’s vision. The GPT compiles this into a precise image prompt, which can be passed to a storyboard artist or  image generation tool to create the storyboard frame.

4 - Test and Refine: After uploading the instructional documents, continuously test and refine the GPT as you gather feedback. Does it ask for the right information? Does it source the correct details? Based on feedback, improve the instructions to enhance clarity and ensure the GPT evolves with the team’s workflow. Iterative testing ensures that the GPT remains efficient and reliable for more complex tasks.

This process allows the GPT to behave like a production assistant, guiding the user through each step in a structured way. The more interactive the GPT is, the more tailored and useful its final output will be, reducing the need for additional revisions.

The Role of the Knowledge Miner

The success of a custom GPT depends heavily on how it is managed and continuously updated. This responsibility falls to a new role in the production process—the Knowledge Miner. First introduced in the article, The Creative Mind: Establishing a Source of Truth Through AI, the Knowledge Miner is responsible for:

• Curating and managing source documents: Ensuring the GPT’s knowledge base is accurate, up-to-date, and reflective of the production’s current needs.
• Writing and updating instructions: Crafting the detailed scenarios and question prompts the GPT uses to interact with users.
• Iterating and refining: Continuously testing and improving the GPT’s performance based on feedback, refining instructions, and updating the knowledge base as new information becomes available.

The Knowledge Miner’s role is essential because they ensure the GPT remains a valuable tool, evolving with the project and adapting to new requirements. They are the custodians of the "source of truth," making sure the custom GPT aligns with the team’s needs while improving its efficiency and precision over time.

Why Custom Instructions Matter

Providing custom instructions enables you to control not only the information the GPT accesses but also how it interacts with users. When working on character design, for example, the GPT can focus solely on the details required for that task. This targeted approach keeps the workflow streamlined and ensures that the GPT remains a valuable production assistant.

Similarly, during scriptwriting or scene development, the GPT can be customized to ask specific questions about tone, action, and setting. This not only saves time but also ensures that the GPT is making decisions aligned with the creative vision, which is especially useful for more complex tasks like adjusting character design or updating scene locations.

The Benefits: Consistency and Precision Across Production

At the end of the day, the true value of a custom GPT lies in how it centralizes and streamlines the production process. By providing consistent and precise information through tailored instructions, the GPT eliminates the need for team members to dig through multiple documents. It becomes a centralized assistant that keeps everyone on the same page.

This guided interaction allows the team to focus on creative tasks, confident that the information they’re receiving is accurate and aligned with the overall vision of the project. This approach also makes complex tasks—such as integrating character feedback or adjusting scenes—easier, as the GPT ensures everything stays consistent.

A Future of Consistent, Collaborative Production

As creative teams work together to bring a story from words on a page to fully realized visual experiences, collaboration and communication are key. However, as teams grow and multiple contributors handle different aspects of production, ensuring consistency can become a logistical challenge. With a custom GPT acting as a centralized assistant, this challenge is significantly reduced.

For example, instead of different departments manually adjusting content to fit a shared vision, the GPT ensures each team's output aligns perfectly from the start. This workflow doesn’t just apply to storyboarding or character creation. The same method can be used to drive scriptwriting, sound design, and even marketing efforts, ensuring every element of production remains aligned with the original vision. A well-instructed GPT becomes the backbone of the entire production, allowing the creative team to focus on innovation and storytelling.

An AI-Driven Production Future

The ability to extract specific, nuanced details from a story and deliver them to a creative team changes the nature of production. Rather than relying on fragmented notes or subjective interpretations, teams can access precise, consistent information at the click of a button. This not only saves time but also ensures the final product—whether a film, animation, graphic novel, or interactive gaming experience—accurately reflects the vision of the source material.

This AI-assisted approach can also extend beyond a single production. Once the GPT has been trained on a particular book, it can be updated or expanded for future projects, sequels, or adaptations in different mediums. The custom GPT essentially becomes a long-term asset for the production company, growing and evolving alongside the content it helps produce.

Conclusion: Building an AI That Works for You

The key to creating an effective custom GPT lies in the instructions you provide. By crafting detailed, scenario-specific instructional documents, you transform your GPT into a powerful tool that not only understands the content but also guides users through each production step. This approach leads to a more efficient, cohesive, and consistent production workflow—where every frame, script, and design remains true to the original vision.

By embedding structured knowledge directly into the GPT, you're not just automating repetitive tasks—you’re ensuring it acts as a true assistant to the production team. When everyone is working from the same, reliable source of truth, you avoid costly mistakes, streamline decision-making, and enhance the overall quality of your creative output.

The future of production is AI-assisted. With the right instruction set, you can harness the full potential of custom GPTs to empower your team, drive creativity, and maintain precision across every phase of the project. With AI doing the heavy lifting, your team can focus on what matters most: bringing ideas to life in the most efficient and consistent way possible.

If you have any questions or thoughts on how AI tools will help or hinder your creative production process, please share them in the comments. If you found this article useful, subscribe for updates on future releases.