Adobe Indigo's AI Camera Coach: Useful, but Experimental

Adobe has placed a generative-AI laboratory unusually close to the shutter button. AI Playground, a limited experiment inside version 1.1 of the Project Indigo iOS camera app, can critique a photograph, propose a better reshoot, remove distracting objects, simulate shallow depth of field, apply styles and accept custom editing prompts. The interesting idea is not another filter. It is feedback while the photographer may still be standing in front of the scene.

The release is also easy to overstate. AI Playground is not a broad commercial launch. Adobe says only a few percent of Indigo users will see it, initially for a few weeks. Access is free and requires no account during the test, but the company may expand, change, stop or eventually charge for it. Anyone choosing a phone or subscription today should treat the feature as experimental, not as a promised reason to buy.

Four tools, with one genuinely new placement

The Playground has four areas. Object Editing offers preset switches for background people, vehicles, signs, wires, rubbish and other clutter, plus a custom removal request. Styles can reinterpret the image as pen and ink, a colour wash, watercolour or another look. Custom Edit accepts an open text prompt. Photo Guidance is the part that best uses the camera context: one button asks an LLM for positive and negative criticism; another proposes changes for a reshoot and edits for the existing file.

Critique can cover framing, light, colour and emotional impact. Adobe is working on making advice aware of the specific phone and lens. That matters because generic photography guidance is often physically impossible: a phone has several fixed-aperture modules, not the continuously adjustable aperture of a dedicated camera lens. Advice to “stop down” or use a focal length the phone lacks is not coaching; it is model-shaped noise.

Project Indigo can potentially do better because it knows which camera captured the image and because the user has not yet left the location. A suggestion to step sideways, change exposure or remove an object from the frame can improve the original capture rather than merely repair it later. Whether the current model gives consistently good advice has not been established by controlled testing. TechCrunch found the feedback descriptive and some removals convincing, but one hands-on report is not a benchmark.

The buttons are prompts in disguise

Adobe presents many actions as toggles because open-ended prompting is unreliable. Behind each button sits a prompt tuned for a narrower task. Pressing it again may still produce a different result. This interface reduces the need to invent prompt wording, but it does not make the generation deterministic.

The first model in the experiment is Google's cloud-based Nano Banana, though Adobe says it may change models, test different ones on different users or use Firefly for some actions. That means both quality and behaviour can change during the experiment. Edited outputs are roughly 2,000 pixels on a side to reduce transfer and generation time, lower than the full-resolution Indigo capture.

There is another technical compromise. Indigo records high-dynamic-range photographs, while current image generators return standard-dynamic-range results. Adobe applies a separate machine-learning process, guided by the original HDR image, to boost the generated result back toward HDR. The company openly says this correction is not always accurate, especially when the generated colours and tones diverge sharply from the capture.

Generated edits can quietly change the truth

Removing a rubbish bin from a holiday picture is different from removing a person from a documentary scene. AI Playground makes both actions easy. Adobe also demonstrates that styles can slightly alter a face and that custom generation can invent structures hidden by fog. These are not edge cases: a generative model synthesises pixels, so it may modify identity, texture or geometry that the user did not ask to change.

For creative images, that can be acceptable if the result is understood as an edit. For journalism, insurance, property records, evidence or product listings, the generated version cannot stand in for the original. The safest workflow keeps the untouched capture, labels material generative edits and checks faces, hands, signs and background objects at full size. A pleasing result is not proof of fidelity.

The critique itself also deserves scepticism. Composition rules are tools, not laws. An LLM can reproduce popular conventions such as the rule of thirds while missing intent, cultural context or the reason a photographer chose an awkward frame. The useful relationship is assistant, not judge: offer alternatives, explain them, and let the photographer decide.

Privacy has two layers

Adobe says participants must agree to anonymous collection of button presses. It says it will not inspect or upload prompts and images to Adobe servers or use them to train models. However, the generative work uses a cloud model and requires internet access, so image data must still be transmitted for processing outside the phone. “Not uploaded to Adobe” is not the same claim as “never leaves the device.”

The terms described for this small experiment may also change if the feature becomes a product. Users should check the in-app consent and current privacy notice rather than relying on a launch article months later. Sensitive images are best kept out of experimental cloud generation unless the processing route and retention rules are acceptable.

Verdict

AI Playground is announced and available to a limited test group, not a finished mass-market feature. Its strongest idea is timely reshoot guidance, because it uses the one advantage a camera app has over a conventional editor: the scene may still be in front of you. The weaknesses are equally instructive—cloud dependence, reduced output resolution, identity drift, non-deterministic edits and advice that may confuse convention with quality.

If Adobe can make the coach device-aware, transparent about processing and reliably conservative with untouched details, it could teach more than another filter carousel. For now, it is a promising experiment to evaluate, not an authority on photography and not a permanent feature to count on.