Kling AI Prompt Format: 6-Part Framework + Examples (2026)

TL;DR: The best Kling AI prompt format is a 6-part framework — subject + action + context + style + camera + motion. Motion language is weighted heavily because Kuaishou trained the model on motion fidelity. Image-to-video is best in class, the Motion Brush lets you paint motion paths, and Kling 2.6+ now generates native audio. Below: 20 copy-pasteable prompts, a camera modifier reference, and negative-prompt tips.

What is the best prompt format for Kling AI?

The best Kling AI prompt format is a 6-part framework: subject + action + context + style + camera + motion. Front-load the subject and the action in the first sentence, then layer context, style, camera, and an explicit motion block. Kling rewards directorial language — describe how things move, not just how they look — because Kuaishou trained the model with heavy emphasis on motion fidelity and physics.

That last point is the whole game. Most people write Kling prompts like a photographer: they describe a beautiful frozen image and hope the model figures out the motion. Kling punishes that. It generates video, and video is motion. The single biggest upgrade you can make to your Kling prompts is to stop thinking like a photographer and start thinking like a director of photography — someone whose job is to describe how the camera and the subject behave over time.

Kling AI is built by the large-model team at Kuaishou, the Chinese short-video giant, and it has become one of the most-used AI video tools on the planet. By the end of 2025, Kling's global user base surpassed 60 million, and the product hit an annualized revenue run rate of around USD 240 million in December 2025. That scale matters for you as a prompter: a huge, fast-iterating user base means the model is tuned hard around the prompt patterns that actually work, and those patterns are stable enough to template.

This guide gives you the exact 6-part structure, explains why motion earns its own block, walks through 20 tested prompts across four genres, and covers the features that make Kling distinct — Motion Brush, best-in-class image-to-video, native audio, and multi-shot sequencing. Everything here is portable across Kling 2.1, 2.5, 2.6, and 3.0; newer versions simply reward more explicit direction.

What are the 6 parts of a Kling prompt?

The 6-part Kling prompt format breaks a shot into ordered building blocks. Each part answers one question, and the order matters — Kling reads the front of the prompt as the priority. If you bury your main subject behind three sentences of atmosphere, the model can lose track of who it's supposed to animate.

Notice the discipline in the Context row: three to five elements, no more. This is one of the most common failure points. Each Kling model has a complexity ceiling, and packing ten environmental details into one shot forces the model to triage — usually by dropping the things you cared about most. Multiple 2026 prompt guides converge on keeping context to three to five elements maximum for clean output, with the lighter Turbo variants preferring three to four.

Here's why the 6-part split outperforms a single run-on paragraph. When you separate concerns, you give yourself a checklist. You can scan your own prompt and ask: Did I name the subject specifically? Did I give exactly one clear action? Is my context under five elements? Did I anchor a style? Did I specify the lens and the move? Did I describe the motion explicitly? Six yes-or-no checks, and you've eliminated 90% of the reasons Kling outputs disappoint.

If you want to go deeper on how structured prompting beats freeform across every model, our prompt engineering fundamentals guide covers the underlying logic that the 6-part framework applies to video specifically.

Why does motion get its own block in Kling?

Motion gets its own block because Kling was trained with a heavier emphasis on motion fidelity than most rivals, and an explicit motion block measurably tightens the result. Where Veo 3 and Sora will respect motion cues embedded inside a scene description, Kling does noticeably better when the motion intent is isolated and stated plainly — including subject motion, camera motion, and the physics of how they interact.

Compare these two prompts for the same shot:

Without explicit motion (weak in Kling):
"A woman walks across a cobblestone street."

With explicit motion (Kling-optimal):
"A woman walks across a wet cobblestone street.
Motion: smooth gimbal tracking from her right side at walking
pace. Subtle horizontal camera drift. Hair moves naturally with
her walking rhythm. Coat sways with each step. Feet make
heel-first contact with the stone, weight transferring forward."

The second version doesn't just look more detailed — it gives Kling physics it can compute. Describing heel-first contact and weight transfer forces the model to calculate ground contact, which is exactly the kind of cue that prevents the floating or sliding feet that wreck so many AI video clips. The model isn't guessing at biomechanics anymore; you handed it the rules.

This is the mental shift that separates good Kling prompters from frustrated ones. A photographer describes a moment. A director of photography describes a movement: the lens behaviour first ("slow dolly push forward"), then the subject's action and its physics, then how the two relate over the duration of the shot. Kling 3.0's own guidance is explicit about this — write prompts like directions to a scene rather than a list of objects, and the model prioritizes cinematic intent over static visual description.

A simple rule of thumb: every Kling prompt should contain at least one verb describing camera movement and at least one verb describing subject movement. If your prompt has zero motion verbs, you're not prompting a video model — you're prompting an image model and hoping.

What does a complete Kling prompt look like?

Here is the 6-part framework fully assembled into a single production-ready prompt. This is the format I template and reuse, swapping the bracketed pieces per shot.

Subject: A 30-year-old woman with curly red hair, light freckles,
wearing a long charcoal wool coat, holding a leather portfolio.

Action: Walking briskly across a wet cobblestone street, glancing
back over her shoulder once, mid-walk.

Context: Paris at dusk in late autumn, light rain falling,
Notre Dame visible in soft-focus background, lamp posts lit,
atmospheric haze.

Style: Cinematic film look, 35mm film grain, golden hour mixed
with cool streetlamp blue, melancholic palette.

Camera: Medium close-up tracking shot from her right side, 35mm
lens, slight handheld feel for intimacy.

Motion: Smooth gimbal arc following at walking pace. Subject
holds frame center-right. Subtle vertical camera bob mimicking
walking rhythm. Hair and coat move naturally with motion. Feet
make heel-first contact, weight transferring forward each step.

Negative: warped face, extra fingers, sliding feet, melting
background, jittery camera, morphing, flickering.

The output: a clean 5- or 10-second clip with reliable subject motion, coherent camera flow, atmospheric consistency, and stable anatomy. The negative prompt at the bottom is your insurance policy — more on that below.

Two structural notes. First, you don't strictly need the literal labels ("Subject:", "Action:") — Kling parses well-written prose too — but labelling forces you to fill every slot, which is the real benefit. Second, this prompt is roughly 130 words, squarely inside the sweet spot. Kling's own guidance and independent testing both land around three to six sentences per shot for the cleanest results.

20 tested Kling prompts (copy-paste templates)

Below are 20 prompt skeletons across four genres. Fill the brackets, keep the 6-part order, and always include a motion block. These are starting points — tune the specifics to your shot.

Cinematic narrative (1–5)

1. Solo character moment

Subject: [character + 3 distinguishing features]
Action: [single beat — looking up, reaching out, exhaling]
Context: [location + time + one atmospheric layer]
Style: cinematic film look, 35mm grain, [palette]
Camera: medium close-up, locked-off static, 35mm lens
Motion: minimal camera; subject performs one slow deliberate action; natural breathing

2. Two-character dialogue

Subject: two people, [descriptions]
Action: in conversation, slight smile from one, considered nod from the other
Context: [setting + two ambient details]
Style: cinematic, [palette]
Camera: medium two-shot, shallow depth of field
Motion: subtle facial micro-expressions, minimal camera drift, natural blinks

3. Tracking shot through environment

Subject: [character], walking purposefully
Action: traverses [location] at a steady pace
Context: [three atmospheric details]
Style: cinematic, [palette]
Camera: medium tracking shot from behind or side, 35mm lens
Motion: smooth gimbal follow at walking pace, gentle drift, heel-first footfalls

4. Slow push-in on object

Subject: [object — letter, photograph, key item]
Action: stationary, dust motes drifting in a shaft of light
Context: [setting — desk, mantel, table]
Style: warm cinematic, shallow depth of field
Camera: dolly push from medium to close-up, 50mm lens
Motion: slow steady forward push; environmental dust drift; flickering candlelight

5. Wide establishing reveal

Subject: [character or anchor element]
Action: stationary; environment moves (clouds, water, leaves)
Context: [vast scene]
Style: cinematic wide, atmospheric haze, golden hour
Camera: wide shot, 24mm lens, slow gimbal arc
Motion: slow sweeping camera reveals subject in environment; foliage sways

Product / commercial (6–10)

6. Hero product turntable

Subject: [product with material + finish details]
Action: rotating slowly on dark walnut surface
Context: studio backdrop, deep shadow
Style: luxury commercial photography, side-lit
Camera: medium close-up, 50mm lens, locked-off static
Motion: smooth 360° turntable rotation; reflections shift across the surface

7. Liquid pour

Subject: [liquid + container]
Action: pouring into a vessel
Context: dark backdrop, single rim light
Style: high-contrast commercial
Camera: medium shot side-on, 50mm
Motion: slow-motion pour, splash dynamics, droplet fall with surface tension

8. Lifestyle product placement

Subject: [product] in a domestic context
Action: stationary; life happens around it (steam, background movement)
Context: [home setting + warm light]
Style: lifestyle commercial, warm hygge
Camera: medium shot, slight angle
Motion: steam rises, light shifts gently, no camera movement

9. Hand-reach product

Subject: [product] on a surface, hand entering frame
Action: hand reaches and lifts the product
Context: [surface + lighting]
Style: clean commercial
Camera: top-down or 3/4 angle, locked
Motion: hand enters from edge, lifts product smoothly out of frame; natural finger flex

10. Reveal from dust

Subject: [product] on a pedestal
Action: a dust cloud parts to reveal the product
Context: dark backdrop, single key light
Style: dramatic commercial
Camera: medium static
Motion: dust dissipates revealing product; product remains perfectly static

Action / kinetic (11–15)

11. Skater trick

Subject: [skater] mid-trick
Action: kickflip / ollie / grind
Context: urban skatepark or street
Style: high-contrast action photography
Camera: low angle, 24mm wide, dynamic
Motion: 60fps slow-motion, board flips, body rotates, weight lands on bent knees

12. Runner at sunrise

Subject: [runner in technical wear]
Action: running on a track
Context: track at dawn, golden first light
Style: athletic commercial
Camera: medium tracking from the side, 35mm
Motion: gimbal moves at the runner's pace, motion-blurred background, arms drive

13. Cooking sequence

Subject: hands [chopping / searing / plating]
Action: continuous cooking motion
Context: warm kitchen, overhead practical light
Style: food editorial
Camera: top-down or 3/4 close-up, 50mm
Motion: rhythmic knife work, steam rises, ingredients tumble naturally

14. Crowd movement

Subject: a market crowd, no central subject
Action: people moving in different directions through the space
Context: marketplace, dappled light
Style: documentary observational
Camera: top-down or high-angle wide, 24mm
Motion: time-lapse-like flow of people; camera locked; consistent foot traffic

15. Vehicle drive-by

Subject: [vehicle] passing
Action: drives across the frame
Context: [environment]
Style: cinematic, [time of day + palette]
Camera: locked-off side-on, 50mm
Motion: vehicle enters left, exits right at speed; motion blur; tyres grip the road

Mood / abstract (16–20)

16. Slow-motion fabric

Subject: silk fabric in wind
Action: undulating motion
Context: dark backdrop or cloud sky
Style: abstract slow-motion
Camera: medium close-up, 85mm
Motion: 120fps slow-motion undulation; gentle gimbal drift; fabric catches light

17. Particles in light

Subject: dust motes / particles
Action: drifting through a shaft of light
Context: dim atmospheric room
Style: ethereal abstract
Camera: medium close-up, 50mm
Motion: particles drift slowly on convection currents; camera locked

18. Liquid macro

Subject: surface tension of [liquid]
Action: a drop falls, ripples spread
Context: black backdrop, side light
Style: macro art photography
Camera: extreme close-up, macro lens
Motion: 240fps ultra slow-motion ripple expansion; concentric waves

19. Time-lapse clouds

Subject: cloud formation
Action: clouds shifting overhead
Context: open sky, golden hour
Style: time-lapse landscape
Camera: locked-off wide, 24mm
Motion: 4× speed cloud movement; light shifts across the sky; no camera motion

20. Geometric morph

Subject: geometric shapes
Action: morphing between forms
Context: neutral abstract space
Style: minimal motion design
Camera: locked-off centered, 50mm
Motion: smooth shape interpolation; clean edges; camera static

Save the ones that work as reusable templates. If you build a few dozen of these, you stop typing structure and start filling brackets — which is exactly the kind of friction our save-and-reuse prompt library is designed to remove.

How do you write camera movements in Kling?

You write Kling camera movements as plain directorial verbs paired with a lens and a framing — for example, "medium close-up, slow dolly push forward, 50mm lens." Kling responds to the same cinematography vocabulary a real crew uses, and it weights this language heavily, so precise camera terms produce more predictable results than vague ones like "cinematic shot."

Use this reference table as your vocabulary palette:

Three rules keep camera language from backfiring:

1. Pick one primary move. "Static camera + tracking shot" is a contradiction the model resolves randomly. Choose locked-off or a move, not both.
2. State how the camera behaves over time. Kling 3.0's guidance stresses explaining the camera's relationship to the subject across the shot's duration, not just a static label — e.g., "camera holds wide for two seconds, then slowly pushes in to a close-up."
3. Match lens to intent. A 24mm wide exaggerates space and motion (great for action); an 85mm compresses and isolates (great for intimate portraits). Telling Kling the focal length nudges the whole composition.

For a side-by-side of how camera vocabulary differs across the major video models, see our Veo 3 vs Kling vs Sora comparison.

How do negative prompts work in Kling AI?

Negative prompts in Kling work by listing artifacts you want the model to avoid, typically in a dedicated negative-prompt box. They are most valuable on high-motion or anatomically tricky shots, where the model is more likely to introduce distortions like warped faces, extra fingers, or sliding feet.

A reliable baseline negative prompt:

warped face, distorted hands, extra fingers, extra limbs,
melting background, morphing, warping, flickering, jittery
camera, sliding feet, floating limbs, text artifacts

For a gritty, photoreal look specifically, prompt guides recommend also excluding smiling, cartoonish, 3D render, smooth plastic skin so the model doesn't default to the glossy, over-smoothed aesthetic AI video tends toward. Excluding terms like morphing, warping, and flickering helps Kling hold a stable image across frames, which is especially important on fast actions where consistency tends to break first.

Treat the negative prompt as a second layer of control, not a magic fix. If your positive prompt is vague, no amount of negative prompting will rescue it. Get the 6-part structure right first, then use negatives to clean up the predictable failure modes.

When should you use Kling's Motion Brush?

Use Kling's Motion Brush when you want to control motion spatially rather than describe it in text — for cinemagraphs, selective motion, precise direction control, or animating an existing brand asset. The Motion Brush lets you paint regions of a reference image and assign each one a direction and intensity, so only the parts you choose come alive.

Motion Brush was introduced in Kling version 1.5 and has been refined since. The workflow:

1. Upload a reference image (or generate one first in Midjourney).
2. Switch to Motion Brush mode.
3. Paint the regions that should move — hair, water, fabric, smoke, vehicles.
4. Set an intensity per region (0–100%).
5. Add direction vectors where it matters (smoke rises up-and-left; a flag streams right).
6. Generate.

Where Motion Brush wins over pure text prompting:

• Cinemagraphs — a mostly still image with a single element animated. These get outsized engagement on social because the eye locks onto the one moving thing.
• Selective motion — water flows while everything else stays frozen. Hard to achieve reliably with text alone.
• Direction control — you need smoke to rise specifically up-and-to-the-left, not "somewhere."
• Brand-asset animation — an approved logo or product photo gets subtle motion without re-rendering the whole frame.

Because you can paint independent vectors, you can guide individual elements in different directions and speeds within one frame — something text prompts struggle to express cleanly.

Why is Kling's image-to-video the best in class?

Kling's image-to-video (I2V) is considered best in class because it preserves the source image's identity, lighting, and composition with unusual fidelity while adding believable motion. You feed it a still you already approve of, and it animates that exact frame rather than reinterpreting it from scratch.

The standard I2V flow:

1. Generate or select a source image.
2. Upload it to Kling I2V mode.
3. Describe motion intent (or use the Motion Brush).
4. Set duration (5s or 10s; 3–15s on Kling 3.0).
5. Aspect ratio is preserved from the source by default.
6. Generate.

Kling's I2V Pro modes support up to 1080p output, and the Pro variants in the 2.1 family add first-and-last-frame conditioning, so you can specify both endpoints of the motion.

When I2V beats text-to-video:

• Brand-consistent imagery — you already have approved, on-brand stills and need them to move.
• Concept exploration — you generated a still you love and just want to see it animate.
• Cost and time control — a Midjourney still plus Kling I2V iterates faster and cheaper than re-rolling text-to-video, where pricing runs roughly USD 0.07–0.14 per second through third-party providers.

The Midjourney → Kling pipeline is the workhorse move for serious creators: nail the composition and style as a still where iteration is cheap, then animate the keeper in Kling. If you're building image prompts for that first step, our Midjourney prompt structure guide pairs directly with this workflow.

Does Kling AI generate audio now?

Yes — as of the Kling 2.6 release, announced December 21, 2025, Kling generates native synchronized audio. That includes dialogue, narration, singing, sound effects, music, and ambient noise, and the model can match sound effects to the on-screen content. Users can also upload custom voices for training, so a character keeps a consistent voice across multiple clips.

This closes what was previously Kling's biggest gap against Veo 3. Earlier versions — 2.1, 2.5 — output silent video that you had to score and dub in post. Now you can prompt audio inline. When you do, the key is to be explicit about who speaks and when: bind dialogue to a character's unique action, describe the action first and the dialogue second, and add tone descriptors. For example:

Action then dialogue:
The detective leans across the table, lowering her voice.
She says, in a tired, gravelly tone: "We both know how this ends."

Kling 3.0's guidance is explicit that in multi-character scenes you should indicate who is speaking and when, and include voice tone, emotion, and accent details per character. Treat audio as one more block in your prompt: subject, action, context, style, camera, motion — and now, when relevant, sound.

What is the difference between Kling 2.5, 2.6, and 3.0?

The difference is a steady climb in speed, audio, and shot complexity. The 6-part framework works on all of them; newer versions simply reward more explicit shot and audio direction. Here's the lineup:

A few practical takeaways from the version map:

• Kling 2.5 is the value workhorse. Its roughly 2× faster generation and ~30% lower cost make it ideal for high-volume iteration where you don't need audio.
• Kling 2.6 is your pick when sound matters. If a shot needs dialogue, a sound effect, or scored ambience baked in, go 2.6+.
• Kling 3.0 unlocks sequences. Its multi-prompt system supports up to six labeled shots in a single generation and flexible durations up to 15 seconds, so a multi-beat performance no longer requires stitching separate clips.

For Kling 3.0 multi-shot, structure your prompt as sequential, clearly labeled shots — "Shot 1: …, Shot 2: …" — each with its own framing, subject, and motion, rather than one compressed paragraph. Establish your core subjects early with consistent, unique labels and avoid pronouns, which the model can lose track of across shots.

How long should a Kling prompt be?

A Kling prompt should run roughly 3 to 6 sentences, or about 100 to 250 words, for a single shot. Below about 60 words the output drifts generic; above 350 the motion intent dilutes and the model starts dropping details. For multi-shot Kling 3.0 sequences, write focused descriptions per shot rather than one giant block.

The reason length matters cuts both ways. Too short, and you've under-specified — the model fills the gaps with its defaults, which is how you get generic, soulless clips. Too long, and you've over-specified — the model can't hold every detail across the frames, so it triages, often dropping the things you cared about. The 3-to-6-sentence band, which Kling's own guidance and independent testers both land on, is the zone where you give enough direction without overwhelming the model.

Three length-management habits:

• One action per shot. If your scene has three beats, that's three shots (or a Kling 3.0 multi-prompt), not one overstuffed prompt.
• Cap context at five elements. This single rule prevents most over-length problems.
• Cut adjectives that don't change the motion. "Beautiful, stunning, gorgeous" add words and no information. "Heel-first, weight forward, hair trailing" add words and physics.

Common Kling mistakes (and how to fix them)

These are the failure patterns I see most often, with the fix for each.

1. Vague motion. "She moves" produces unpredictable, often janky movement. Fix: specify the motion — "she walks slowly, gentle hair sway, a subtle change in expression, heel-first footfalls."
2. Conflicting camera instructions. "Static camera + tracking shot" confuses the output. Fix: pick one primary camera behaviour per shot.
3. One prompt for a long sequence. Outside Kling 3.0's multi-prompt mode, native clips cap at 5 or 10 seconds. Fix: generate per shot and edit together, or use Kling 3.0's labeled multi-shot.
4. Skipping the style block. "Cinematic" alone is generic. Fix: be specific — "35mm film grain, golden-hour palette, anamorphic lens flare."
5. Forgetting aspect ratio. Default is 16:9. Fix: specify 9:16 for Stories and Reels, 1:1 for square feeds.
6. Burying the subject. Three sentences of atmosphere before you name the character means the model may forget to animate them. Fix: put the subject in the first sentence.
7. Photographer brain. Describing a frozen image and hoping for motion. Fix: add explicit camera-movement verbs and subject-movement verbs, every time.

Power moves for advanced Kling prompting

Once the fundamentals are automatic, these techniques separate professional output from hobbyist output.

1. Use the Midjourney → Kling pipeline. Generate a striking, on-brand still in Midjourney where iteration is cheap, then animate the keeper through Kling I2V. This is the most reliable route to controlled, repeatable results.
2. Motion Brush for cinemagraphs. A mostly still image with one element alive — steam rising, hair moving, water rippling — reads as premium and earns strong engagement on social.
3. Save 6-part templates with placeholders. Build a library of {{subject}} / {{action}} / {{context}} skeletons so you fill brackets instead of retyping structure. Our Global Variables workflow makes swapping recurring values across many prompts trivial.
4. Combine T2V and I2V. Generate the environment with text-to-video, animate the hero subject with image-to-video, and composite them in post for shots neither mode produces cleanly alone.
5. Lead with physics on action shots. "Heel-first contact, weight transfer, arms driving" gives the model the biomechanics it needs to avoid the floating, sliding artifacts that ruin kinetic clips.
6. On Kling 3.0, design for the cut. Plan your six shots like an editor — establishing wide, then the action, then the reaction — so the multi-prompt output already has a rhythm instead of six disconnected beats.

The structure is the skill. The 6-part framework is the same discipline underneath whether you're typing it by hand or letting a tool scaffold it for you. Prompt Architects ships Kling-ready 6-part templates, Global Variables, and a reusable prompt library through both the web app and the Chrome extension — so you keep the structure and skip the typing friction.

Frequently asked questions

What's the best prompt format for Kling AI? The strongest Kling AI prompt format is a 6-part framework: subject + action + context + style + camera + motion. Front-load the subject and action; camera and motion modifiers go in the back half. Kling responds especially well to explicit motion descriptions because Kuaishou trained the model with heavy emphasis on motion fidelity and physics.

How long should a Kling prompt be? Aim for 3 to 6 sentences, roughly 100 to 250 words, for a single shot. Below about 60 words the output drifts generic; above 350 the motion intent dilutes. For Kling 3.0 multi-shot sequences, label each shot and keep each shot's description focused rather than writing one giant paragraph.

How is Kling different from Veo 3 prompt-wise? Kling weights motion and camera language more heavily, so phrases like "slow gimbal arc orbit" or "fast whip pan" land tighter than equivalent wording in Veo 3. Kling also offers a Motion Brush to paint motion paths onto reference images, and its image-to-video is widely considered best in class.

Does Kling generate audio? Yes, as of the Kling 2.6 release (announced December 21, 2025) Kling generates native synchronized audio, including dialogue, sound effects, music, and ambient noise, and supports custom voice training. Earlier versions like 2.1 and 2.5 output silent video that you score in post.

Should I use text-to-video or image-to-video in Kling? Use image-to-video (I2V) when you have a reference still you love; Kling's I2V preserves source identity, lighting, and composition while adding motion. Use text-to-video (T2V) for original concepts. A common pro pipeline is to generate the still in Midjourney first, then animate it through Kling I2V.

What is the Kling Motion Brush and when should I use it? Motion Brush is a Kling feature (introduced in version 1.5) that lets you paint motion regions and direction vectors onto a reference image instead of describing motion in text. It is ideal for cinemagraphs, selective motion, precise direction control, and animating brand assets where only one element should move.

Do negative prompts work in Kling AI? Yes. A negative prompt box lets you exclude common artifacts. Useful terms include "warped faces, extra fingers, melting background, jittery camera, sliding feet, morphing, flickering." Negative prompts are most valuable on high-motion or anatomically tricky shots where the model is more likely to introduce distortions.

What is the difference between Kling 2.5, 2.6, and 3.0? Kling 2.5 brought faster, cheaper generation at up to 1080p. Kling 2.6 added native audio and voice control. Kling 3.0 added a multi-prompt system of up to six labeled shots and flexible durations from 3 to 15 seconds. The 6-part prompt framework works across all of them; newer versions simply reward more explicit shot and audio direction.

By Nafiul Hasan — Founder of Prompt Architects, where we build prompt-enhancement tooling for ChatGPT, Claude, Gemini, Midjourney, Veo 3, and Kling. Last updated: June 10, 2026.