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.

Automatic extraction of product attribute-value pairs from unstructured text like product descriptions is an important problem for e-commerce companies. The attribute schema typically varies from one category of products (which will be referred as vertical) to another. This leads to extreme annotation efforts for training of supervised deep sequence labeling models such as LSTM-CRF, and consequently not enough labeled data for some vertical-attribute pairs. In this work, we propose a technique for alleviating this problem by using annotated data from related verticals in a multi-task learning framework. Our approach relies on availability of similar attributes (labels) in another related vertical. Our model jointly learns the similarity between attributes of the two verticals along with the model parameters for the sequence tagging model. The main advantage of our approach is that it does not need any prior annotation of attribute similarity. Our system has been tested with datasets of size more than 10000 from a large e-commerce company in India. We perform detailed experiments to show that our method indeed increases the macro-F1 scores for attribute value extraction in general, and for labels with low training data in particular. We also report top labels from other verticals that contribute towards learning of particular labels.