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# System prepended metadata

title: ai-lab-workshop-20251008-initial-planning

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# ai-lab-workshop-20251008-initial-planning

Perfect — tomorrow’s session (Oct 8) is **Media Production with AI: Prep (Session 1 of 3)**, so this is the one focused on **pre-production**: planning, research, storyboarding, scheduling, and concept visualization.
Below is a **long brainstorm list** of things you could do, organized by type so you can mix and match depending on the group’s energy and what tech setup you have ready in the Lab.

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## 🧭 Framing and Setup

* **Quick framing talk (5 min):** how pre-production determines the success of any media project, and how AI lets us compress the “thinking → making” loop.
* **Prompt scaffolding mini-demo:** show how structured prompts can replace traditional shot lists, treatments, or mood boards.
* **The “producer’s triangle”:** fast ↔ cheap ↔ good—then ask how AI might change that constraint.

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## 🧠 Concept Development

* **Idea generation sprint:** give everyone a shared theme (“migration,” “time,” “thresholds”) and 10 min to co-develop a concept outline with ChatGPT or Claude.
* **Genre pivot exercise:** have AI rewrite a concept across genres (documentary → horror → rom-com) to illustrate tonal flexibility.
* **AI as creative partner:** prompt comparison—same idea pitched to 3 models, discuss differences in narrative logic and aesthetic taste.

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## 🎬 Storyboarding & Visualization

* **Text-to-image storyboards:**

  * Use DALL·E 3 or Midjourney to generate visual sequences from scripts.
  * Compare coherence across models.
  * Discuss prompt tokens like *“wide-angle cinematic lighting”* vs *“flat storyboard sketch.”*
* **Camera grammar exercise:** have Claude or Gemini output a JSON shot list (`scene`, `shot type`, `motion`, `duration`), then visualize it.
* **AI animatic:** feed the storyboard frames to Runway or Pika to get quick motion tests.
* **Prompt translation:** show how a screenplay paragraph → image prompt → generated frame → refined prompt chain can evolve.

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## 📅 Planning, Scheduling, & Logistics

* **AI producer assistant:** demonstrate prompting ChatGPT Edu to generate call sheets, budgets, and production timelines.
* **Constraint play:** “We only have one camera, 2 hours, and a single hallway.” Let AI propose creative shooting plans.
* **Cast/crew matrix:** generate hypothetical credits and role descriptions to see how AI handles interpersonal logistics.
* **Notion/Sheets automation:** build an AI-generated production plan imported into Google Sheets or Airtable.

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## 🎭 Script, Voice, and Tone

* **Script style translation:** same scene written by AI in three tonal registers (academic, comedic, noir).
* **Table-read with voice models:** use ElevenLabs or ChatGPT Voice to audition AI voice actors.
* **Dialogue polishing:** paste human-written dialogue, have the model suggest pacing/beat adjustments, then critique its taste.

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## 🎨 Visual Moodboarding

* **Collective moodboard wall:** project a stream of generated images; participants upvote or sketch alternatives.
* **Prompt dissection:** show how adding art-historical or cinematic references changes style.
* **Cross-model remix:** take one prompt and pass it through DALL·E → Stable Diffusion → Ideogram to illustrate model bias and “house look.”
* **Style library exercise:** build a shared “Harvard AI Visual Lexicon” board—participants tag prompts and results for future projects.

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## 🧩 Workflow Design

* **From chat to pipeline:** show how a text conversation can become a structured workflow (e.g., generate shot list → export → Storyboarder → Premiere).
* **API preview:** brief glimpse of Realtime API or Whisper for logging interviews in later sessions.
* **Version control for ideas:** demonstrate naming conventions, file versioning, or Git for media projects.

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## 🧍 Hands-On Team Activities

* **3-person mini-crews:** writer + designer + producer each prompt different stages; recombine outputs.
* **“AI Pitch Meeting”:** teams develop a one-minute AI-generated pitch deck (3 slides, 1 image, 1 tagline, 1 budget line).
* **Lightning showcase:** end with quick 60-second presentations of each project’s AI-assisted pre-production materials.

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## 🧩 Reflection & Critical Layer

* **Bias and taste discussion:** how model training data affects visual language (e.g., gendered camera gaze).
* **Ethical reflection:** synthetic casting, likeness rights, and authorship.
* **Meta-prompting:** have participants ask the AI to critique its own suggestions (“Why did you choose this shot?”).
* **Archivability:** how to store and cite AI-generated pre-production material in academic or creative workflows.

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## 🧰 Possible Tools to Feature

| Category          | Tools                                                                   |
| ----------------- | ----------------------------------------------------------------------- |
| Chat interfaces   | ChatGPT Edu (4o mini), Claude 3.5 Sonnet, Gemini 1.5 Pro                |
| Visual generation | DALL·E 3 in ChatGPT, Midjourney v6, Ideogram, Pika Labs video           |
| Storyboarding     | Storyboarder (WonderUnit), Notion AI whiteboards, Figma AI plugin       |
| Planning          | ChatGPT Advanced Data Analysis, Google Sheets + GPT add-on, Airtable AI |
| Scriptwriting     | Sudowrite, ChatGPT Edu Custom GPT “Script Doctor”                       |
| Voice/Audio       | ElevenLabs, ChatGPT Voice, Whisper Live                                 |
| Scheduling        | Notion Calendar, AI Google Calendar Assistant                           |

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Would you like me to turn this brainstorm into a **structured 90-minute run-of-show** (with time blocks, goals, and materials) for tomorrow’s session?




Perfect — those new directions push this session into a **richer hybrid of analog + digital media prototyping**, which aligns beautifully with the Learning Lab’s ethos. Here’s an expanded brainstorm structured around your three threads — **Nano-Banana & ingredients**, **line fidelity & AI visual precision**, and **pitch deck / lookbook assembly** — plus a few linking ideas that can make the whole workshop feel like one coherent experiment in *AI-supported creative pre-production*.

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## 🍌 **Nano-Banana + Ingredients: AI Thinking with Tangible Media**

*(for context: Nano-Banana = an LLM “sketching” playground that can integrate “ingredients” like text snippets, prompts, and small files)*

### 1. Ingredient-Based Prompt Crafting

* **Physical Inputs Table:** lay out a “mise en scène” of analog items — postcards, markers, fragments of text, paper textures, screenshots, printed photos.
  Participants choose 3–5 and feed their short descriptions into Nano-Banana as “ingredients.”
  → Output: a storyboard seed, a concept treatment, or a lookbook moodline.
  → Meta-point: showing how *prompting as collage* parallels analog montage.

### 2. Analog–Digital Roundtrip

* Participants **sketch an idea on paper**, photograph it, and upload it as an “ingredient.”
  Nano-Banana then expands the sketch into a concept or storyboard panel.
  → Demonstrates the creative feedback loop between analog thinking and LLM remixing.

### 3. Ingredient Archetypes

* Introduce ingredient “types”:

  * 📄 *Textual (script excerpts, poetic fragments)*
  * 🎨 *Visual (color palette, texture, sketch)*
  * 🔉 *Aural (vibe descriptor, song lyric)*
  * 🧭 *Conceptual (theme, constraint, or emotion)*
    Participants label their ingredients, and Nano-Banana combines them algorithmically.
    → This can evolve into a structured creative taxonomy for future workshops.

### 4. Ingredient Remix Game

* Teams exchange one ingredient with another group (physical or digital) and re-run Nano-Banana with the altered set.
  → The exercise shows how even small prompt perturbations shift the aesthetic dramatically.

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## ✏️ **Line Fidelity & AI Visual Precision**

### 5. Line-Driven Visual Prompts

* Show how to *condition AI image generation* on specific line drawings:

  * **DALL·E 3 “style reference” uploads** — use a scanned sketch or pencil drawing to constrain composition.
  * **ControlNet / Scribble mode** — demonstrate how edge maps guide Stable Diffusion output.
  * **Ideogram “match drawing style” prompts** — test prompt weighting for line vs. fill fidelity.

### 6. Progressive Constraint Pipeline

1. Start with a hand-drawn storyboard panel.
2. Generate a high-fidelity AI image that respects contours.
3. Regenerate with style modifiers (engraving, lithograph, blueprint).
4. Export variations to build a visual language library for the project.
   → You could display these on the projector as a “from hand to machine” timeline.

### 7. Physical–Digital Hybrid Output

* Use tracing paper, light tables, or overhead projectors:

  * Trace AI outputs to reassert human gesture.
  * Rescan the traced version → feed back into AI → show how human touch reconditions the model.
    → Creates an embodied understanding of “co-authorship” between artist and AI.

### 8. Error Aesthetics

* Deliberately push models into misalignment:

  * Over-contrast sketches.
  * Ask for “edge hallucinations” or “dream in ink.”
    → Opens discussion about productive failure and ambiguity as design tools.

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## 📘 **Pitch Decks, Lookbooks, and AI-Generated Presentations**

### 9. Auto-Deck Builder

* Use ChatGPT’s **Canvas or File Upload** to turn a concept document + 3 images into a **slide deck outline**.
* Then refine slide-by-slide:

  * *Slide 1:* Title & tag line
  * *Slide 2:* Concept summary
  * *Slide 3:* Moodboard / visual references
  * *Slide 4:* Technical approach
  * *Slide 5:* Schedule / resources

### 10. Dynamic Lookbooks

* Use **Notion**, **Figma**, or **Pitch.com** to live-assemble lookbooks with:

  * AI-generated visuals from earlier exercises
  * color palettes extracted by Claude or ChatGPT’s Vision tools
  * text snippets (loglines, tone statements, keywords)
* Bonus: generate “tone comparison spreads” (e.g., *how it looks if Wes Anderson directed it vs. Wong Kar-Wai*).

### 11. StoryWorld Decks

* Show participants how to create a “World Bible” deck — maps, mood shots, key props, and style cues.

  * ChatGPT or Claude can write world summaries in production-binder format.
  * Combine text + visuals in Google Slides or Figma live.
  * Encourage participants to include a “feeling palette”: emotions or temporal rhythms.

### 12. AI Deck Persona

* Let Nano-Banana or ChatGPT adopt the persona of a **creative producer or studio exec**, and have participants “pitch” their deck to it.

  * The model gives structured feedback (“logline clarity,” “visual coherence,” “market pitch strength”).
  * Turns AI into a rehearsal partner for real-world pitches.

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## ⚗️ **Hybrid and Meta Experiments**

### 13. “Reverse Ingredient” Challenge

* Feed Nano-Banana a finished AI image or deck and ask it to infer what ingredients might have produced it.
  → Highlights the interpretive nature of AI “reading” media.

### 14. Concept → Script → Board Chain

* Chain models:

  * ChatGPT: concept synopsis → Claude: script draft → DALL·E: storyboard panels → Nano-Banana: lookbook assembly.
  * End with discussion on interoperability and “pipeline literacy.”

### 15. Tangible Pitch Wall

* Print AI-generated visuals on cardstock, pin them up, and let participants arrange decks physically.

  * Photograph final arrangements as analog lookbooks.
  * Optionally, use a webcam feed + CLIP model to auto-tag themes (“urban decay,” “ritual,” “thresholds”).

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## 🧩 **Linkages to Broader Themes**

* **Human–AI Co-authorship:** gesture, materiality, and iteration.
* **AI as Translation Layer:** moving between modalities (sketch → prompt → deck).
* **Studio Pedagogy:** how embodied and social practices can be preserved in AI workflows.
* **Context Engineering:** designing metadata, tone cues, and “ingredient ontologies” for media projects.

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Would you like me to draft a **90-minute plan** that integrates these (e.g., Nano-Banana ingredients → sketch-to-AI → deck assembly → reflection), with suggested timings, prompts, and materials list (markers, cameras, scanning stations, etc.)?



That’s *exactly* the right instinct — to use the studio’s equipment **symbolically and atmospherically** in Session 1 (“Prep”) without crossing into actual **production workflows** that you’ll reserve for “On Set.”
The key move is to make the physical studio act as a **thinking instrument**, not a recording environment yet.
Below are layered ways to bring in cameras, lights, screens, and projectors for *ideation, framing, and visualization* — rather than shooting.

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## 🎥 **Cameras as Idea Machines (Not Capture Devices)**

### 1. Camera-as-Viewfinder

* Mount a camera or phone on a tripod but keep it **unplugged from recording**.
  Let participants look through the lens to *frame compositions* for AI storyboards.
  → Then describe or photograph the framing for prompt input (“medium close-up from low angle, tungsten key, shallow DOF”).

  * This turns the camera into a *prompt-building reference tool*.

### 2. Shot Grammar Exercise

* Place 2–3 cameras around the room aimed at blank backdrops.
  Participants walk between them to *feel* what “coverage” means.
  → They then script or prompt the same micro-scene from each camera’s POV.
  → Connect to later AI shot-listing in Session 2.

### 3. Lens-as-Metaphor

* Offer lenses (or simulated focal lengths on phones) as *creative constraints*:
  “What’s the 24 mm version of your concept? What’s the 85 mm version?”
  → Participants translate those physical optics into AI prompt parameters (wide, telephoto, portrait compression).

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## 🟩 **Green Screen as Concept Canvas**

### 4. Projection Surface for Imagination

* Instead of compositing, use the green screen as a **live projection wall** for evolving AI imagery (storyboards, color palettes).

  * Run a looping slide deck of participants’ generated frames.
  * Lights low, slow fade transitions → transforms the studio into a “thinking cinema.”

### 5. “Standing in the Scene”

* Invite participants to stand in front of the projected AI scene, *not for filming*, but to **embody scale, light, and composition**.

  * Have them describe how being “in” their AI image changes their prompt choices.
  * Take stills (optionally) for reflection, not production assets.

### 6. Green-Screen Palette Wall

* Tape printed color swatches or keywords to the green surface.

  * These serve as analog “tags” for AI generation (“tone: elegiac,” “light: sodium vapor”).
  * Photograph the wall → upload to Nano-Banana as an “ingredient board.”

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## 🖥️ **Screens and Projectors as Collaborative Instruments**

### 7. Multi-Screen Moodboard Loop

* Set up the Lab’s projectors or screens to cycle through:

  * Participant prompts
  * Generated images
  * Live updates from Nano-Banana
* Treat it like a *media aquarium*: ideas swimming across screens while teams work.

  * Great ambient energy; reinforces AI as part of the studio ecology.

### 8. Split-Screen Comparisons

* Side-by-side projection: hand-drawn sketches ↔ AI renderings ↔ modified re-prompts.

  * Invite micro-critiques (“where does the machine misread the hand?”).
  * Builds visual literacy before Session 2’s technical compositing.

### 9. “Projection Mapping Lite”

* Use one projector to cast simple geometric frames or grids onto paper or props on a table.

  * Participants sketch within those light contours — a pre-visualization of how light and frame interact.
  * Photograph the sketch under projection and feed it to the model for iteration.

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## 💡 **Studio as a Pre-Production Playground**

### 10. Lighting as Metaphor

* Set up 3-point lighting rigs but use them theatrically:

  * Toggle between warm/cool scenes as participants discuss mood.
  * Let the “lighting state” correspond to AI tone cues (“noir,” “pastoral,” “clinical”).
  * No recording — just embodied understanding of light language.

### 11. Spatial Storyboarding

* Use masking tape on the floor to block out a scene.

  * Participants mark camera, subject, prop positions.
  * Label with sticky notes: “Close-up,” “Cutaway,” “Insert.”
  * Photograph layout and upload to AI for automatic diagram interpretation.
    → Serves as bridge to Session 2’s real blocking exercises.

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## 🧩 **Integrative Nano-Banana + Studio Idea**

### 12. Ingredient Stations in Physical Space

* Place QR codes or NFC tags around the room:

  * “Camera,” “Lighting,” “Set,” “Sound,” “Mood.”
  * Scanning one sends an “ingredient” to Nano-Banana.
  * Participants physically *collect* ingredients by moving through the studio.
    → Turns spatial exploration into prompt-building.

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## 🧠 **Save for Session 2 (“On Set”)**

To maintain a clear pedagogical progression:

* **Avoid:** live recording, chroma key compositing, real-time transcription, or AI camera control.
* **Do:** concept visualization, framing practice, projection feedback, embodied composition.
  This way, Session 2 can feel like a natural escalation from *imagination space → capture space.*

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Would you like me to sketch a **hybrid floor plan + time flow** showing how these activities could unfold across the Lab’s physical zones (tables, projection wall, green screen, lighting corner)? It’d give you a visual map of how the pre-production “studio ecology” could operate tomorrow.



---

That’s a great and very strategic question. Editors, technical directors, and post-production people are actually *perfect* participants for a pre-production workshop—if you design it to foreground the *structural thinking*, metadata, and pipeline logic that make a production succeed later.

Below is a brainstorm of **pre-production workflows that resonate deeply with editors, post supervisors, and technical crew**, grouped by mindset.
All of them stay comfortably in the “Prep” phase—no cameras rolling yet—but tap into the habits and logics editors already live by.

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## 🧩 1. **Thinking Like a Postperson in Pre-Production**

Editors and tech leads are naturally *reverse-engineers*. You can frame pre-production as “designing the edit before you shoot.”

### a. Edit-First Storyboarding

* Build a **“paper timeline”** using stills or AI frames: sequence them left-to-right on a wall or in Figma/Storyboarder.
* Editors annotate: *What will this cut feel like? Where would you want coverage?*
* Then feed that markup into ChatGPT or Claude to generate a **shot list** automatically from editorial intention.

### b. Data Management & Naming Logic

* Simulate file-naming schema early:
  `PROJ_SC01_SH03_V01.mov` → show how LLMs can enforce or generate consistent naming conventions and metadata templates.
* Editors immediately recognize this as sanity insurance for future workflows.

### c. Proxy Thinking

* Generate **AI proxy assets** (temporary dailies): low-res, text-to-image stand-ins for scenes.
  → Editors can cut with these in mind to test pacing before footage exists.

### d. Conform & Metadata Design

* Have participants define **metadata schemas** for assets *before production*:
  `scene, location, take_quality, emotion, sound_notes`.
  → Show how these schemas can later feed into automated transcription, tagging, or RAG workflows.

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## 🧮 2. **Technical Planning & Pipeline Design**

### a. Folder Architecture Exercise

* Groups design a **directory tree** that anticipates post workflows.
  → Then prompt an LLM to generate automation scripts that would create that tree on a drive.

  * Example: “Create a bash script that builds our media project folder structure.”

### b. File Round-Trip Simulation

* “Fake ingest”: drop a few images/audio clips into a shared folder.

  * Ask AI to generate a *pre-flight report* (“missing metadata,” “duplicate names”).
    → Editors love this—it’s pre-production QA.

### c. LUT and Color Style Planning

* Instead of grading, generate **AI lookbook stills** that express color space intention: “Teal-orange grade,” “bleach bypass,” “Fuji Eterna.”
  → Editors can imagine how the final look will cut together; colorists appreciate being consulted early.

### d. Audio Tone Previz

* Use text-to-sound models or libraries to create **mood beds**—placeholder ambiences or emotional arcs.
  → Editors recognize this as temp music or reference tone that informs pacing.

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## 📖 3. **Script-to-Edit Continuity Planning**

### a. Script Breakdown Automation

* Run scripts through Claude or ChatGPT to extract:

  * Locations
  * Characters
  * Props
  * Shot tags
    → Feeds directly into scheduling and editing prep (what will need continuity checks).

### b. Continuity & Coverage Maps

* Generate heatmaps or diagrams of scene transitions, coverage density, or expected cutting rhythm.

  * For example: “Where do we expect jump cuts vs. match cuts?”
    → Editors enjoy visualizing editorial energy before footage exists.

### c. Pre-Labeling for Transcripts

* Build **tag taxonomies** editors will later apply to transcripts (topics, tone, characters).
  → Use AI to auto-suggest tags, then refine as a team.

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## 🎞️ 4. **Editorial Aesthetics in Pre-Vis**

### a. “Edit Intent” Annotation

* Take AI storyboards or Nano-Banana frames and have editors label:

  * “Cut on motion”
  * “Hold for 3 sec”
  * “J-cut / L-cut opportunity.”
    → Those notes can become structured prompt inputs (“Generate a storyboard anticipating J-cuts between dialogue lines.”).

### b. Montage Exercises

* Give everyone a shared text corpus (say, student interviews or public-domain footage descriptions).

  * Have AI assemble *written montages*—juxtapositions, repetitions, visual rhymes.
    → Editors will instinctively recognize Eisensteinian principles encoded in language.

### c. Pre-Visualized Rhythms

* Use **pacing simulations**: LLM generates timestamps or beats per minute for scenes, visualized as waveform-like timelines.
  → Editors discuss how this anticipates rhythm and emotion.

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## 🖥️ 5. **Tools & Cross-Department Handshakes**

### a. AI-Assisted Paper Edit

* Give them raw transcript text (from a hypothetical doc shoot).

  * Let ChatGPT condense it into a *paper edit outline* (soundbite order).
  * Editors evaluate and critique the AI’s editorial decisions.

### b. Pre-Production as Schema Design

* Frame pre-production as **schema thinking**—what structured data will make post easy?

  * Shot logs
  * Audio notes
  * Scene metadata
  * AI-assisted ingest logs

### c. ChatGPT + Airtable + Resolve API demo

* Show how you could, in theory, export an Airtable shot list → CSV → Resolve metadata panel.

  * They’ll instantly see the utility for post workflows.

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## ⚙️ 6. **Integrating the Studio Hardware Lightly**

Editors and techs will appreciate the **signal flow** aspects:

* Use the green screen stage as a **metadata playground**: tag imaginary shots (“wide green setup,” “close interview setup”) and simulate ingest forms.
* Project AI-generated look references on the screens—then label them with LUT intentions or exposure notes.
* Run a camera tethered to a monitor purely to simulate *ingest path*, not capture.
  → “What metadata could travel with this file?” becomes a creative question.

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## 💬 7. **Meta-Conversation Prompts**

* *“If you could redesign pre-production to eliminate one post headache, what would it be?”*
* *“How could AI help you get dailies that cut together better?”*
* *“What metadata do you wish directors understood?”*

These prompts let technical participants surface process wisdom while still playing creatively.

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If you want, I can sketch a **version of tomorrow’s run-of-show oriented toward editors/tech participants**, weaving in a few Nano-Banana “ingredient” stations and light studio use.
Would you like that?
