Class 5, hybrid format

Methods and prompts

Five methods to take with you

METHOD 01 · TAUGHT 7:03 part of: structure your ask

Write it as Format, then Subject, Details, Style and Parameters

The image companion to GOLD. Five slots, filled in order, so you know exactly which word is doing which job when an output goes wrong.

Where it came fromSydney presented the image prompt structure as the visual companion to GOLD, five slots filled in order, format, subject, details, style, and parameters, so a stuck output can be traced back to exactly which word in which slot caused the problem.Use it whenUse this as the starting structure any time you write an image prompt from scratch, so each descriptive word has a clear job.

Same idea elsewhereGive it two keyframes instead of one description (class 2) · Speak the language of the internet (class 3) · Write it as a GOLD block (class 4)

Working prompt

Format: [photo, painting, tintype, etc.]. Subject: [the hero]. Details: [what's happening]. Style: [the vibe, colors, textures, feelings]. Parameters: [aspect ratio, weirdness, character ref]. Start with exactly this, nothing extra.

You will know it worked whenthe image matches the five fields you filled in, with nothing extra added that you didn't specify in any of them.

METHOD 02 · TAUGHT 7:45 part of: build tools not chats

Weight your references on purpose

Composition and style are separate dials, and whichever one you max out wins. State the split explicitly, even in a tool without weight sliders.

Where it came fromSydney demonstrated with a tulip image that setting a style reference at full weight made the AI hallucinate to copy grain texture, while setting a composition reference at full weight dropped the described subject entirely, and that splitting both references around fifty percent found the actual balance.Use it whenUse this when you are working from both a composition reference and a style reference and one of them keeps overriding your prompt.

Same idea elsewhereStand up a reusable Project or Gem (class 4) · Let one model write the prompt for another (class 6) · Let the agent build the bones (class 8)

Working prompt

Use this as my composition reference at roughly 50% strength, and this as my style reference at roughly 50% strength: influence the output, don't copy either one directly. My prompt: [describe the subject and scene].

You will know it worked whenthe output echoes both references without copying either one outright, and the composition and style read like separate, blended influences.

METHOD 03 · TAUGHT 7:52 part of: context beats prompts

Evolve the reference instead of restarting

Feed your best imperfect output back in as the new reference and change exactly one thing. Every generation is a stepping stone, not a discard.

Where it came fromSydney described feeding her own imperfect output, a haunted-looking horse with perfect haze and framing, back in as the new reference and changing only one variable at a time, treating every generation as a stepping stone rather than something to discard.Use it whenUse this once you have a generated image that gets part of what you want right, so you build on it instead of starting over from scratch.

Same idea elsewhereControl the input and build gradually (class 2) · Show it your voice and not just the output (class 3) · Build the turnaround sheet before you animate anything (class 6)

Working prompt

Here's my last output [attach]: the [lighting/framing/haze] is exactly right, but the [specific element] is wrong. Use this as the new reference. Change only the [one variable] and keep everything else as close to identical as possible.

You will know it worked whenonly the one element you named actually changed, and everything else in the new output still matches your last one.

METHOD 04 · TAUGHT 7:57 part of: reject the first draft part of: keep the judgment human

Diagnose your own hallucination first

Before handing a bad output to the model for troubleshooting, name your own guess. The five-step check works better when you've already staked a claim it can confirm or correct.

Where it came fromSydney's five-step hallucination check has you analyze the output, check your references, evaluate your prompt for vague or sticky words, consider the training data, and only then bring it to a multimodal tool, and she said the check works better when you have already staked a guess it can confirm or correct.Use it whenUse this when an image output has gone wrong and you want to diagnose it systematically before asking AI to fix it.

Same idea elsewhereJudge AI output with your own trained eye (class 1) · The sea-of-sameness check (class 2) · Pick the relationship on purpose (class 2)

Working prompt

Here's my output and my prompt: [attach both]. I answer first, then you check me. My guess at what went wrong: [your diagnosis: reference, prompt wording, or training data]. Now walk the five-step check yourself and tell me where my read was right or wrong.

You will know it worked whenit states plainly whether your guess about the reference, the wording, or the training data was right, not a generic list of causes.

METHOD 05 · TAUGHT 8:51 part of: context beats prompts

Study your own image and then extend it

Conversational consistency the way ChatGPT did it for the ocean series: analyze an existing image's visual logic before asking for new subjects, and re-attach the original every round to fight drift.

Where it came fromSydney's conversational consistency method, illustrated with an ocean image series, has the model analyze an existing image's visual logic before generating new subjects in the same style, and re-attaching the original image every round to fight drift.Use it whenUse this when you want a series of new images that stay visually consistent with one image you already like.

Same idea elsewhereControl the input and build gradually (class 2) · Show it your voice and not just the output (class 3) · Build the turnaround sheet before you animate anything (class 6)

Working prompt

I want more images in the same style as this one [attach]. Analyze its style, color, and rendering first. Then suggest new subjects that fit the same collection. Every time we generate one, I'll re-attach this original as the reference, not the desired output.

You will know it worked whenthe new subjects it suggests share the color and rendering it just described from your original, not a random unrelated set.