Creative search platforms help users turn short, vague queries into searches and generation prompts. To be useful, a suggestion has to fit the context it appears in, the asset type, the season, and the user’s region, and it has to be grounded in assets the platform can actually retrieve. A fluent suggestion that returns nothing wastes the user’s attention and erodes trust. Building such a system is hard because there is no ready-made training data: user logs are sparse, noisy, and privacy-constrained, and they say nothing about context or grounding. We first solve this with corpus-grounded synthetic supervision. A two-stage offline pipeline turns 59M asset captions into context-aware training data, constrained by a Creative Knowledge Graph and grounded through retrieval, and fine-tunes a single low-latency model that serves related suggestions, refinements, and generation-ready prompts in one retrieval-free step. We check grounding by running suggestions against the production index: offline, retrieval grounding raises the share of well-grounded suggestions by over 30% relative, and in production about 99% of served suggestions return real assets (a null rate near 1%). The deployed system is preferred over the prior log-based suggester in human evaluation and improved the primary business metric in production. As the system reached new surfaces, two requirements emerged that the supervised data met poorly: single-word suggestions for space-constrained interfaces, and selective seasonal and regional context—added in the harder cases the deployed model missed, and suppressed where it does not apply. Rather than regenerate the data and retrain, we add a Group Relative Policy Optimization (GRPO) stage whose hybrid reward pairs an LLM judge with programmatic checks for format, length, and diversity. It improves seasonal alignment by 18.6 and regional alignment by 5.2 points (on a 0 to 100 judge scale) and raises single-word compliance from 15.1% to 99.1%, a format absent from the supervised data, while keeping relevance and diversity intact and leaving the retrieval-free serving path unchanged.