Why Image-Model Prompting Is a Different Skill

prompt1 already covers prompting in real depth — clarity, context, constraints, role prompting, few-shot examples, chain-of-thought, iterative refinement. If prompting an image model were just "the same skill applied to pictures," this course wouldn't need to exist; it could be a single bonus chapter tacked onto the end of prompt1. It isn't, because the thing being prompted is a genuinely different kind of system.

What prompt1 Actually Assumes

prompt1-2's own anatomy of a good prompt — Clarity, Context, Constraints, Output Format — rests on one load-bearing assumption: that the model being prompted can follow an instruction. Claude reads "summarize this in three bullet points, using a formal tone" and does something recognizable as obeying that instruction, because Claude was trained, via RLHF and instruction-tuning, specifically to map instructions to compliant behavior. Every technique prompt1 teaches — role prompting (prompt1-3), few-shot examples (prompt1-4), chain-of-thought (prompt1-5) — is a way of shaping how an instruction-following system carries out an instruction.

An image model was never trained to do that.

What an Image Model Was Actually Trained to Do

Tools like Midjourney, Stable Diffusion, and DALL-E are built on diffusion models (the full mechanism is imgai1-2's own subject — this chapter only needs the shape of the idea). During training, the model sees millions of (image, text caption) pairs and learns a statistical association between patches of text and patches of visual pattern — "a photo of a golden retriever" pulls the generation process toward pixel arrangements statistically associated with that caption in the training data. There is no step anywhere in that process resembling "read this sentence, form an understanding of what is being asked, and comply." The model has no representation of your request as a request at all — only as a string of text to condition image generation on.

Descriptor Composition, Not Instruction-Giving

If an image prompt isn't an instruction, what is it? The working model this course builds toward, starting concretely in imgai1-3: a prompt is closer to a dense list of descriptors — subject, style, composition, lighting, medium — than to a sentence directed at a listener. "Write a poem about the ocean" (an instruction, aimed at prompt1's own kind of model) and "a weathered lighthouse at dusk, oil painting, dramatic lighting, wide shot" (a descriptor composition, aimed at this course's own kind of model) are doing fundamentally different jobs, even though both are grammatically similar-looking strings of English.

Three Tools, Three Real Trade-offs

This course doesn't teach one image model — it teaches three, deliberately chosen because each makes a genuinely different trade-off, not because "more tools" is inherently better coverage:

• Midjourney (imgai1-4) — cloud-only, Discord-based, a distinctive bracketed --parameter syntax, and a well-documented painterly/stylized bias baked into the model itself.
• Stable Diffusion (imgai1-5) — open-source, can run entirely locally, and exposes real technical knobs (CFG scale, sampler choice, checkpoints, LoRAs) no other tool in this course offers.
• DALL-E (imgai1-6) — integrated directly into ChatGPT's own conversational interface, which makes it, genuinely, the one tool in this course closest to prompt1's own territory. It's covered last specifically so that convergence lands as a real observation, not a starting assumption.

Hands-On Exercises