For unknown domains or where training on contextual data is impractical, generic statistical language models (SLMs) provide accurate speech recognition but are not easily modified to suit specific applications. We describe a class of automatic speech recognition solutions, based on generic SLMs, where the developer has an option to improve accuracy by providing configuration hints at runtime in the form of grammars or vocabulary lists. We cite performance examples and case studies from live deployments.