The CARE Framework: Context, Anchor, Restrict, Exemplify

Context is everything the model needs to know about the situation before it starts generating. Who is the audience? What's the purpose? What's the background? What does the model need to understand to give a useful response?

Most people skip context because it feels obvious to them. It isn't obvious to the model. Without context, the model defaults to the most generic interpretation of your request. With it, the model can tailor its output to your specific situation.

For LLMs: "I'm writing a product description for a £400 moisturiser targeting women 35–50 who buy from Net-a-Porter. They're informed buyers who find over-explained ingredient lists patronising."

For image prompts: The environment, time of day, and setting are context. "Rain-soaked Tokyo street at midnight, neon reflections on wet pavement" sets the context for every visual decision that follows.

An anchor is a specific reference point that locks the model's interpretation to something concrete. It can be a named style, a real camera, a publication, a person, a format, or anything else that carries specific meaning the model has been trained on.

Anchors do something that descriptive language can't: they invoke the full context of whatever they reference. "Shot in the style of Roger Deakins" doesn't just say "cinematic lighting." It says: natural practical sources, restrained colour, deep shadow, the specific quality of light that made No Country for Old Men look like that.

For LLMs: "Write in the tone of a Kinfolk magazine feature" or "Format as a two-column comparison table." Both are anchors — they invoke a specific, recognisable output the model has encountered in training.

For image prompts: The camera and lens are the most reliable anchor. "Canon EOS R5, 85mm f/1.2" anchors the output to the depth, compression, and quality associated with that specific combination.

Restrictions are the things you don't want. They're easy to overlook because they feel like negatives — you're describing absence, not presence. But they're often the most important part of a prompt because they eliminate the model's most predictable failure modes.

Without restrictions, models default to their highest-probability outputs. For image models that means plastic-looking skin, flat lighting, stock photo composition. For LLMs it means corporate filler phrases, hollow enthusiasm, vague generalisations.

For LLMs: "No filler phrases. Don't start sentences with 'I'. No exclamation marks. Avoid the words 'curate', 'elevate', 'transform'. Under 100 words."

For image models: The negative prompt is the restriction field. CGI, plastic skin, airbrushed, overexposed, watermark, blurry — put your most common failure modes here every time.

Showing the model an example of what you want is the single most reliable way to get consistent output. Examples communicate things that descriptions can't. A model that's shown a reference image, a sample paragraph, or a worked example will reproduce the pattern far more reliably than one given only an abstract description.

For LLMs: a few-shot prompt includes examples of input-output pairs before your actual request. The model reads the pattern and applies it. Even one example — "Here's a product description that's close to what I want. Write something similar for this product:" — significantly improves results.

For image models: reference images via ControlNet IP-Adapter or conversational iteration in ChatGPT Image 2.0 are the equivalent. Showing is always more precise than describing.

Even if you can't provide a literal example, you can describe one: "The tone should feel like the copy on Aesop's product pages — dry, specific, no superlatives." That's a reference the model can use.