AI Image Prompting: The Complete 2026 Guide

Key takeaways:

• The model landscape split into four clear shapes: parameter-driven (Midjourney V7), conversational (DALL-E / ChatGPT images), open-weights controllable (Stable Diffusion, Flux Pro), and ecosystem-bound (Imagen, Firefly, Ideogram). The prompt anatomy is shared; the dialect is not.
• Six slots carry across every model — subject, style, lighting, composition, mood, technical. If you cannot name a slot, the model picks one for you, and usually picks the generic one.
• Dialects differ in three dimensions: how the model receives structure (parameter flags vs. natural language vs. weighted tokens), whether it supports negatives, and how it handles references (style references, character references, or LoRAs).
• Consistency across a set is a prompt-architecture problem, not a feature. Seeds, character references, and locked vocabulary do most of the work.
• Evaluation is slot-by-slot faithfulness, not a feeling. "It looks cool" is not the same as "it matches the brief" — and confusing the two is how image pipelines quietly drift.
• Style references are communication with the model, not summoning rituals. Stacking twenty adjectives does not make a better image; naming the right style once does.
• Image prompting and video prompting share the anatomy but diverge on motion, time, and continuity. When the idea needs a before and after, move up the modality ladder.

Two years ago, writing an AI image prompt felt like incantation — pile enough adjectives on top of each other and hope the model caught the vibe. In 2026 that style still works, occasionally, for screenshots you will throw away. It does not work for a shoot, a campaign, a product catalog, or anything that has to look consistent across ten images. What works in 2026 is a brief — the same six slots any art director would brief a photographer with, translated into the dialect of whichever model you picked.

This pillar consolidates the SurePrompts image-generation cluster into a canonical entry point. Each section links out to the deep-dive post for that tool or workflow. Use this page to find the right tool, learn the shared anatomy, and know where to go next. For the opinionated how-to on the anatomy itself, the companion deep dive is how to write AI image prompts. For the prompt-engineering foundation this builds on, see our pillar on context engineering — the general discipline image prompting sits inside.

What an AI Image Prompt Actually Is in 2026

An AI image prompt is a structured description — sometimes text alone, sometimes text plus reference images — that a generative model uses to produce an image. The key word is structured. In 2024 most prompts were flat: one long paragraph of adjectives and commas. In 2026 the good ones are structured by slot.

Image prompting is a form of multimodal prompting: you are writing text that must survive translation into the image modality. That translation is not free. A model's text encoder reads your prompt, maps it into a semantic space, and the diffusion or generative backbone produces pixels conditioned on that mapping. Tokens that are weak signals — "beautiful," "stunning," "amazing" — burn capacity without steering anything. Tokens that are strong signals — "35mm lens," "Rembrandt lighting," "isometric," "matte painting" — actually change the output. Writing a good prompt is writing strong signals.

It is also different from text prompting in two important ways. First, the model cannot "ask a clarifying question" the way a chat model does — it has one shot at interpreting your brief, so ambiguity becomes a silent failure. Second, success criteria are harder to articulate than in text tasks; "the email sounds professional" is measurable, "the image looks right" is not, unless you decompose it. Both facts push you toward explicit, slot-based briefs over loose descriptions.

The vision-language-model underneath most 2026 image systems means the model has some grounding in how text and images co-occur in real-world data — but that grounding is statistical, not logical. If "cyberpunk alley" appears frequently with specific visual cues in training data, you get those cues. If you want something the training distribution rarely saw, no amount of adjective-stacking summons it — you need references, specific vocabulary, or a tool-specific control (LoRA, style reference, ControlNet) to pull the model toward the region you want.

The 2026 Model Landscape

The image-generation market is no longer a one-horse race. Each major model has a distinct personality, a distinct control surface, and a distinct commercial posture. Picking the right model before you write is half the work.

A few threads to pull on.

Midjourney V7 is the model with the sharpest control surface and the most distinctive house style. Its Discord-first (now also web) interface rewards fast iteration, and its parameter system makes it the easiest model to learn intentionally. If you are doing editorial, stylized, or look-development work, start here. The Midjourney V7 prompting guide is the deep dive.

DALL-E inside ChatGPT has pulled ahead on conversational iteration. Because it lives inside a chat model, you can describe what you want, see the result, ask for a variation, and keep refining — all without re-stating the whole brief every time. The trade-off is less fine-grained control: no explicit seed parameter, no style reference flags in the Midjourney sense. See ChatGPT image prompts in 2026 for the conversational workflow.

Stable Diffusion (SDXL, SD3) is the open-weights baseline. Its strength is not raw output quality — several hosted models beat it there — but total pipeline ownership. You run it locally, you pick the sampler, you stack LoRAs, you wire ControlNet for structural conditioning, you fine-tune for your own character or product. If your workflow has repeatable subjects, controlled compositions, or production constraints that cloud models cannot meet, Stable Diffusion is the answer.

Flux Pro is the newer open-weights-plus-hosted entrant that has become the go-to for photorealism. It follows natural-language prompts closely and has strong adherence to detailed briefs. See the Flux Pro prompting guide for the specifics.

Imagen, Ideogram, and Firefly each serve a specific niche. Imagen is the Gemini-native option — valuable if your workflow is already in Google's stack. Ideogram is the text-in-image specialist; if your image needs to contain legible words (a poster, a mockup, a brand asset), it is the fastest route there. Firefly is Adobe's commercial-safe option — trained on licensed and public-domain data, which matters for enterprise workflows where training-data provenance is contractually required.

For a head-to-head on the two most common default choices, see Midjourney vs. DALL-E in 2026. For how the image side compares to the video side, see the cross-modal Midjourney V7 vs. Sora 2 vs. Runway vs. Veo 3 comparison.

The Universal Prompt Anatomy

Every strong image prompt — regardless of model — covers six slots. You can omit slots on purpose. You cannot forget they exist. When a slot is missing, the model fills it with a plausible default, and the default is almost always generic.

1. Subject. What the image is of. The specific noun, the specific entity, the specific action. "A woman in a red coat walking across a rain-slicked street" is a subject. "A woman" is not. Specificity in the subject slot pays the highest compounding returns — every other slot lands better when the subject is precise.

2. Style. The visual idiom. Art movement, medium, period, reference. "Oil painting in the style of late Dutch Golden Age" names a region of visual space. "Cyberpunk, 80s anime, detailed" names three non-orthogonal directions and leaves the model to pick. Name one style region clearly, then modulate with secondary descriptors.

3. Lighting. The light source, direction, quality, and time of day where relevant. "Golden hour, low sun from the left, long shadows" is lighting. "Dramatic lighting" is not — it is a hope. Cinematographers have a full vocabulary for this (rim light, fill light, Rembrandt, chiaroscuro, high-key, low-key) and models understand it well.

4. Composition. Framing, lens, angle, aspect ratio. "Low-angle three-quarter view, 35mm lens, subject in left third" is composition. "Nice shot" is not. Composition is where most image prompts leak signal — model defaults tend toward centered, eye-level, 50mm-equivalent framing, and if that is not what you want, you have to say so.

5. Mood. The emotional tone. "Melancholy, quiet, introspective" versus "joyful, frenetic, chaotic" — the model reads these as real steering signals. Mood is the slot most easily overloaded with empty adjectives; a single honest mood word beats three aspirational ones.

6. Technical. Aspect ratio, resolution, seed, sampler (where applicable), negative prompt. These are not the finishing touches — aspect ratio especially changes composition itself. Pick them before you generate, and lock them across a set.

A worked example. Prompt without slot discipline:

A cool warrior in dramatic lighting, epic, beautiful, highly detailed, masterpiece, cinematic

Prompt with slot discipline:

A weathered samurai in lacquered armor resting against a stone lantern, oil painting in the style of late Kuniyoshi, low sun filtering through bamboo from the upper right casting dappled shadows, three-quarter low-angle framing at 35mm, quiet and resolved, 3:2 aspect ratio

The second one is not longer because it is more ornate — it is longer because it actually fills the slots. Every word does work.

The full walkthrough of the anatomy, with worked examples and common failure modes, is in how to write AI image prompts.

Model-Specific Prompt Dialects

The six slots are shared. How you express them differs by model. Here is the dialect layer.

Midjourney dialect. Parameter flags are the primary control surface. --ar 16:9 sets aspect ratio. --stylize (commonly 0–1000) controls how aggressively Midjourney applies its house aesthetic — low values for realism, high for its distinctive look. --chaos (0–100) controls how much variance across the four-image grid. --seed <n> fixes the initialization. --sref <url> passes a style reference image. --cref <url> passes a character reference for consistency. --no <terms> excludes content. Treat the natural-language part of the prompt as the brief, and the flags as the technical slot.

DALL-E dialect. Full natural language. You tend to get better results describing the scene conversationally than stacking comma-separated tokens. Because DALL-E lives inside ChatGPT, you can iterate by message — "make the lighting warmer, keep everything else" — and the thread carries context. No explicit --seed parameter, so reproducibility across sessions is harder; within a single conversation, though, consistency is strong.

Stable Diffusion and Flux dialect. Keywords and weights. Prompts tend to look like portrait of a weathered samurai, lacquered armor, (golden hour:1.2), bamboo forest, oil painting, volumetric light, 35mm with a matching negative prompt like blurry, low quality, extra fingers, watermark, text. Weights in parentheses ((term:1.2)) amplify a token; [term:0.8] attenuates it. For structural control beyond prompt text, you reach for ControlNet (pose, depth, edge-conditioning), IP-Adapter (image-prompt transfer), and LoRAs (lightweight fine-tunes for specific subjects or styles). See the negative prompting glossary entry for the mechanics.

Imagen dialect. Instruction-style framing often lands well — "Generate an image of..." followed by a scene description. Long, specific descriptions work better than stacked keywords. Imagen tends to be aggressive about safety and content-policy filtering; prompts that run afoul of filters fail silently or return modified outputs.

The portable rule: write the brief once in natural language, then translate to dialect. A prompt engineer who learns the dialect translation step saves themselves from re-discovering the same brief five times.

Model-Specific Deep Dives

Short orientation per model, with a pointer to the full deep dive.

Midjourney V7

V7 is the model with the strongest control surface and the most distinctive house style. Its parameter system (--ar, --stylize, --chaos, --seed, --sref, --cref, --no) is the reason professionals reach for it for editorial and look-development work — no other model lets you turn style intensity up and down on a slider, lock a character reference across a shoot, or run structured A/B variance via --chaos this cleanly. The trade-off is the learning curve and the Discord/web native interface.

For the complete parameter reference and the prompt-structure playbook, go to the Midjourney V7 prompting guide.

DALL-E / ChatGPT Images

DALL-E's strength is conversational iteration. You write a prompt, see the result, ask for a change, and the thread carries the context. You can say "keep everything the same but change the lighting to golden hour" and get a meaningful variation, not a full re-roll. The limitation is fine-grained parametric control — there is no explicit seed, no --stylize knob, no style-reference flag in the Midjourney sense.

For the conversational-iteration playbook, see ChatGPT image prompts in 2026.

Flux Pro

Flux has pulled ahead on photorealism and prompt adherence. It reads long, specific, natural-language prompts closely and does not impose a strong house aesthetic, which means you can drive it toward the look you want without fighting a default style. It is available as a hosted API and for local deployment, which matters for pipelines.

For the full Flux playbook including guidance scale, seed usage, and the photoreal-specific vocabulary that works, see the Flux Pro prompting guide.

Stable Diffusion (SDXL, SD3)

Stable Diffusion is where you go when you need ownership. Local deployment. Custom models. LoRA fine-tuning for a specific character, product, or brand aesthetic. ControlNet for structural conditioning — pose, depth, edges. IP-Adapter for image-prompt transfer. The raw output is less impressive out of the box than Midjourney or Flux, but the pipeline ceiling is much higher.

The full SD workflow is outside this pillar's scope — the short pointer is: treat it as a pipeline, not a one-shot generator, and invest in the control tools (ControlNet, LoRA, IP-Adapter) before you invest in prompt engineering. Prompt wording matters less when ControlNet is doing the composition work.

Composition, Lighting, and Style — The Shared Vocabulary

A working image prompter's vocabulary is not "more adjectives." It is specific terms from photography, cinematography, and art history. Models were trained on the internet's description of those terms, so using them correctly produces reliable, repeatable results.

Lighting terms that work.

Lens and composition terms that work.

Style vocabulary — a quick note on ethics. Art-movement, medium, and period vocabulary is safe and expressive: impressionist, Bauhaus, ukiyo-e, Art Deco, mid-century modern, Dutch Golden Age, film noir, matte painting, watercolor, gouache, charcoal. Specific-artist references are a gray zone. Deceased artists whose work has aged into art-historical reference are broadly accepted. Living artists whose style is being cloned for commercial output is contested — ethically, and in some jurisdictions legally. Adobe Firefly's commercially-safe training posture restricts living-artist references entirely. Other platforms allow them, but the practice invites debate. The neutral stance we take: reach for movement/medium/period vocabulary first, and use specific-artist references only when no broader term communicates what you want.

A practical rule: three strong style words that point to the same region of visual space beat ten words that point in different directions. "Dark academia, oil painting, late 19th century" is coherent. "Dark academia, cyberpunk, anime, oil painting, watercolor, 3D render" asks the model to average six incompatible styles and gives you the blurry mean of all of them.

Advanced Patterns

Once the fundamentals are working, the advanced control surface is where production work happens.

Image-to-image (img2img). Feed the model a starting image and a prompt, and the model transforms the image toward the prompt. Useful for style transfer, rough-sketch-to-final, or iterating a specific composition. Available in Stable Diffusion, Flux, and to varying degrees in Midjourney (via image prompts) and DALL-E (via the edit workflow).

Inpainting and outpainting. Inpainting masks a region of an image and regenerates only inside the mask — useful for fixing hands, changing an object, or swapping a background. Outpainting extends the canvas beyond the original image. Both are first-class in Stable Diffusion and available in DALL-E's edit modes; Midjourney supports them via Zoom Out and Vary (Region).

Character consistency. The headline use case for multi-image sets. Midjourney's --cref feature passes a character reference image and attempts to keep the character consistent across new generations. DALL-E maintains character consistency within a conversation thread. Stable Diffusion workflows use LoRA fine-tuning on the character (the most reliable approach) or IP-Adapter for lightweight reference. For shoots that demand true consistency across dozens of images, a LoRA-based SD pipeline is still the most reliable tool; the hosted models are closing the gap, not at parity.

Prompt weighting. Stable Diffusion and Flux support parenthetical weighting — (golden hour:1.3) amplifies the term, [watermark:0.5] attenuates it. Midjourney supports :: weighting (red dress::2 blue dress::1). Use it sparingly: weighting is a scalpel, not a sledgehammer. If you need weight 2.0 on a term for the prompt to work, the term is probably wrong or fighting another term, and you should rewrite.

Negative prompts. Where supported (Stable Diffusion, Flux, Midjourney via --no), negatives exclude content. A standard negative-prompt baseline for photoreal work is something like blurry, low quality, extra fingers, watermark, text, jpeg artifacts, disfigured. Do not turn the negative prompt into a wishlist — every negative token costs capacity. Keep it short and focused on failure modes you actually see.

Seed control. Seeds fix the random initialization. Same prompt + same seed = same (or near-same) output. Locking the seed lets you change one slot at a time and see its isolated effect — the single most important technique for iterative refinement. This is the image-prompting analog of few-shot prompting for text: you isolate one variable and observe the delta. Midjourney, Stable Diffusion, and Flux expose seeds directly. DALL-E does not in the same way, though the conversational thread provides soft consistency.

References and control nets. For precise compositional control in Stable Diffusion, ControlNet conditions generation on structural inputs — pose skeletons, depth maps, Canny edge maps, normal maps. Give it a pose, get a generation in that pose. Give it a depth map of a room, get a new room with the same spatial layout. This is how production SD workflows get repeatable composition across a set.

The iterative refinement loop — generate, evaluate, adjust one slot, regenerate — is the same loop we recommend for any agentic prompt work. Image prompting is a form of agentic work: the model is the agent, the prompt is the spec, and the seed is the stop condition.

Specialized Workflows — Where to Go Deeper

Four niches where the image-gen cluster goes past the general pillar and into the specific craft.

Product photography. Product work has hard constraints — brand colors, consistent lighting, neutral backgrounds, clear focus, multiple angles. Midjourney V7 in particular has become a common tool here, with style references locking a look across a catalog and character references extended to product references for consistency. See Midjourney V7 for product photographers for the product-specific playbook — studio lighting prompts, background control, and the "shoot a product from three angles" workflow.

Fashion and editorial. Fashion work asks the model to hold style, pose, and garment detail across a look. It rewards precise vocabulary — fabric terms, cut terms, era terms — and aggressive style-reference use. See Midjourney V7 for fashion editorial for the editorial playbook, including pose direction, garment-detail prompts, and the multi-image continuity pattern.

Animation and VFX. Image models are increasingly used for pre-production work in animation and VFX — concept art, style frames, asset reference, texture generation. The constraints are consistency across frames, adherence to an established art direction, and integration with downstream pipelines. See Midjourney V7 for animation and VFX for the pre-production workflow.

Text-in-image. If your output needs to contain legible text — a poster, a sign, a mockup — Ideogram is the specialist. Midjourney V7 has improved on text legibility but still misses on long strings; SD and Flux are inconsistent. For commercial-safe brand work with text, Firefly plus Adobe's typography tools is often the better route than a single-prompt approach.

Commercial-safe work. When training-data provenance matters contractually — enterprise brand work, large-scale campaigns with legal review — Firefly is the default because its training data is licensed and public-domain. Other models may or may not suit depending on your specific legal constraints and platform terms. This is a question for legal, not for prompting.

Evaluating Image Outputs — Beyond "Does It Look Good"

"It looks cool" and "it matches the brief" are different standards. An image can satisfy the first and fail the second completely, and the failure is often silent because the image is still attractive. A disciplined evaluation checks the brief, not the vibe.

A practical checklist.

• Subject faithfulness. Is the thing in the image the thing you asked for? Is it in the state, action, or configuration you asked for? Count fingers, check proportions, verify material. Models have gotten dramatically better at fingers, but failure modes persist.
• Style faithfulness. Does the visual idiom match? Not "is it stylized," but "is it the specific style you named." If you asked for Art Deco and got generic retro, that is a miss.
• Lighting faithfulness. Is the light source, direction, and quality what you specified? A subject lit from the wrong side is a silent failure — the image still "looks lit."
• Composition faithfulness. Is the framing, angle, and aspect ratio what you specified? Default-centered output when you asked for rule-of-thirds is a miss.
• Mood. Subjective, but honestly readable. If you asked for melancholy and got cheerful, something in the prompt is fighting itself.
• Consistency across a set. When generating more than one image, do the images hang together? Same character, same style, same lighting family. This is where seeds, references, and locked vocabulary earn their keep.
• Text legibility. If the image contains text, is the text correct and readable?
• Policy and bias check. Does the output perpetuate stereotypes you did not ask for? Does it contain anything the platform prohibits?
• Licensing and rights. Is the output licensed for your intended use? Does the model's training data or output policy match your deployment context?

We are not claiming SurePrompts has a shipped automated rubric for image evaluation. Honesty matters here — the text-side SurePrompts quality rubric is real and applies to text prompts. The image-side equivalent is, for now, the manual checklist above. Build it into your workflow as a visible step, not a vague intention, and you will catch misses that otherwise ship.

A related point on iteration discipline. When an image is close but wrong, the temptation is to re-roll and hope. Re-rolling is A/B testing on a slot machine. A better loop: identify which slot is wrong (subject, style, lighting, composition, mood, technical), fix that slot specifically, lock the seed, and regenerate. You will converge faster and learn more about the model in the process.

Image vs. Video Prompting

Image prompting and video prompting share the six-slot anatomy. Video adds three more: motion (what moves, how, at what speed), camera (dolly, pan, zoom, tracking, static), and duration (clip length, pacing). The shared vocabulary carries over — lighting terms, composition terms, style vocabulary — but video introduces time as a load-bearing dimension, which changes the evaluation criteria entirely.

If your idea can be expressed as a single frame, stay in image. If the idea requires a before and after — a product rotating, a character performing an action, a shot that establishes and then moves — you need video. For the video side, the SurePrompts cluster covers Veo 3 prompting, Sora 2 prompts, and the Veo 3 vs. Sora 2 vs. Runway comparison. The cross-modal Midjourney V7 vs. Sora 2 vs. Runway vs. Veo 3 comparison is the single best entry point if you are choosing between static and motion output for a specific project.

A useful thought experiment: when you find yourself writing a prompt that contains "then" or "starts... ends..." you have drifted into video territory. Image models interpret temporal language by flattening it into a single moment, usually the last one described. If you meant "a hand reaches for a cup," the model will render either the reach mid-motion or the hand on the cup — it cannot render both. If you need both, you need video.

Failure Modes

Five anti-patterns that quietly wreck image-gen work.

• Prompt soup. Stacking twenty adjectives and three conflicting styles. "Cinematic, epic, beautiful, highly detailed, masterpiece, 8k, sharp, realistic, dreamy, mystical, cyberpunk, film noir, watercolor." The model averages everything and gives you a generic, unfocused result. Cure: fill the six slots, stop adding words once each slot is filled.
• Style cargo culting. Copying prompt snippets from Reddit or a prompt marketplace without knowing what each token does. "Trending on ArtStation" used to do something; mostly does not now. "Unreal Engine 5" rarely changes the output the way people assume. Cure: every token in your prompt should earn its place — if you cannot describe why a term is there, remove it.
• Ignoring aspect-ratio defaults. Models default to 1:1 (Midjourney, DALL-E) or 16:9 (some Stable Diffusion configurations). Aspect ratio changes composition — a portrait framed at 1:1 is a different image from the same portrait at 3:4 at 16:9. Cure: set aspect ratio first, not last.
• Anthropomorphizing parameters. Treating --stylize as "how stylish" or --chaos as "creativity" or CFG as "how much it listens." These parameters have specific mechanics and sweet spots, not personality traits. Cure: read the parameter docs for the model you are using, find the sweet spot by running a ladder (e.g., --stylize 50, 250, 500, 750), and pick by output, not by intuition.
• Chasing single-shot perfection instead of iterating with seeds. Re-rolling the same prompt thirty times and picking the best of the batch. Fifty variations of a shaky prompt is how cost runs up and quality does not. Cure: when close, lock the seed and iterate slot by slot. When not close, rewrite the brief. The middle ground — re-rolling forever — is the expensive failure mode.

Our Position

Six opinionated stances we hold on 2026 image prompting.

• Pick one model for a project and learn its dialect well. Jumping between Midjourney, DALL-E, Flux, and Stable Diffusion for each image spreads your learning thin and your outputs inconsistent. For a given project, pick the model whose dialect fits the work, and get good at that one dialect. Generalize later.
• Style references are communication with the model, not summoning rituals. You are telling the model which region of visual space you want. Name the region once, clearly. Do not stack five redundant style cues hoping one lands.
• Seed + one prompt beats fifty variations. Locking the seed and iterating slot by slot teaches you more in three generations than fifty re-rolls teach you. It also costs less. It also ships faster.
• Structure beats ornament. Six slots filled cleanly beat ten adjectives piled together. The longest and most ornate prompt is rarely the best one; the most structured one usually is.
• Evaluate against the brief, not the vibe. The most important skill is the discipline to ask "does this match what I asked for" after the image generates, not "do I like it." Liking an image that does not match is how pipelines drift.
• Art-movement vocabulary before specific-artist references. Period, medium, and movement terms are expressive, safe, and widely understood by the models. Living-artist references are contested, narrower, and often unnecessary. Start broad, reach for specific only when broad does not communicate.

Related Reading

The SurePrompts image-gen cluster and the frameworks it rests on.

• Foundations. How to write AI image prompts — the anatomy deep dive that this pillar consolidates.
• Midjourney. Midjourney V7 prompting guide — parameter reference and prompt structure. Midjourney V7 for product photographers. Midjourney V7 for fashion editorial. Midjourney V7 for animation and VFX.
• DALL-E / ChatGPT. ChatGPT image prompts in 2026 — conversational iteration playbook.
• Flux. Flux Pro prompting guide — photoreal-first prompt patterns.
• Model comparisons. Midjourney vs. DALL-E in 2026. Midjourney V7 vs. Sora 2 vs. Runway vs. Veo 3 — the cross-modal comparison.
• Video cluster. Veo 3 prompt guide. Sora 2 prompts guide. Veo 3 vs. Sora 2 vs. Runway comparison.
• Frameworks. SurePrompts quality rubric — the text-side rubric and its applicable parts for image briefs. RCAF prompt structure — the four-part structure that generalizes beyond image work. Agentic Prompt Stack — the iterative refinement loop that image prompting is a case of. Context Engineering Maturity Model.
• Pillars. Context Engineering: The 2026 Replacement for Prompt Engineering — the broader discipline image prompting sits inside.
• Glossary. Prompt engineering. Multimodal prompting. Multi-modal. Vision-language model. Negative prompting. Few-shot prompting. Prompt template. Prompt chaining. LoRA.

Image prompting in 2026 is a brief-writing discipline with a dialect layer on top. Pick the model first, fill the six slots, translate into the dialect, iterate with seeds, evaluate against the brief. The beautiful one-shot prompt you get lucky with is memorable — the repeatable process that gets you a good image on the third try, every time, is what actually ships.