Consumer Behaviour
Prompt-shaping behaviour
How users refine the language of their prompts mid-conversation to get a better answer. Reveals a shift from keyword behaviour (typing fewer terms) to conversation behaviour (typing more, more naturally).
What it is
Prompt-shaping behaviour is the way users refine the wording of their prompts mid-conversation to steer an assistant towards a better answer. It marks a shift from one-off keyword entry to iterative conversation.
Why it matters
These refinements reveal the true intent and the gaps in the first answer, giving a clearer picture of need than a single query and signalling which content actually resolves a request.
How it works
It is observed by tracking how a prompt evolves across turns: added constraints, requested formats, clarifications, and corrections that move the conversation towards a satisfying result.
When it applies
It applies during multi-turn interactions with a generative assistant rather than single-shot search.
Examples
- A user follows a broad question with make it shorter and for beginners.
- Someone adds a constraint such as only options under a set budget after seeing the first reply.
- A searcher reframes a vague request into a specific scenario to get a targeted answer.
How it is measured
- Average number of refinement turns per conversation
- Rate of added constraints or format requests across turns
- Correction or clarification frequency within a session
- Turn at which the user accepts the answer and stops
Related terms in Consumer Behaviour
- Agentic browserA browser (or browsing layer) that uses an LLM agent to interpret pages, summarise content, and take actions on behalf of the user. Arc Search, Perplexity Comet, Browser Company's Dia, Dia browser, and similar.
- Agentic commerceAgentic commerce is buying and selling in which an AI agent completes part or all of the purchase on a person's behalf, from finding products to placing the order. Instead of browsing a site, the shopper states an intent and the agent searches, compares, fills the cart and sometimes pays. It shifts the point of decision away from the merchant's own interface and towards the assistant.
- App discoveryApp discovery is the set of ways people find and decide to install mobile apps, including store search and browse, editorial placements, web search, social feeds, advertising, AI assistants and word of mouth. It covers the whole path from first awareness to tapping install. Growth teams study it to work out which surfaces create demand and which only capture it.
- Brand demandSearch volume for branded terms. In an AI-search world, brand demand is the single strongest moat, generic queries are absorbed by AI Overviews and ChatGPT, while branded queries route users directly to brand properties.
- Brand discoveryBrand discovery is the process by which someone encounters a brand for the first time, or becomes newly aware it can solve their problem. It happens across search results, AI assistants, social feeds, communities, creators, reviews, marketplaces and word of mouth. Marketers study it to work out which surfaces introduce them to future customers.
- Consumer behaviour signalObservable patterns in how users phrase queries, refine searches, and choose answers, used by both ranking systems and generative models to infer intent and quality.
Primary research · August 2026
How ChatGPT Shortlists Software Brands
An audit across 10 categories and 60 buying questions. I recorded what ChatGPT reads, throws away and links to when a buyer asks it which software to buy, and what that decides.
- 60
- Questions asked
- 10
- Software markets
- 2,680
- Results read
- 367
- Links shown
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