We performed a diachronic analysis of sound and language in toy commercials, leveraging retrieval-augmented generation (RAG) and open-weight language models in low-resource settings. A pool of 2508 UK toy advertisements spanning 14 years was semi-automatically annotated, integrating thematic coding of transcripts with audio annotation. With our RAG pipeline, we thematically coded and classified commercials by gender-target audience (feminine, masculine, or mixed) achieving substantial inter-coder reliability. In parallel, a music-focused multitask model was applied to annotate affective and mid-level musical perceptual attributes, enabling multimodal discourse analysis. Our findings reveal significant diachronic shifts and enduring patterns. Soundtracks classified as energizing registered an overall increase across distinct themes and audiences, but such increase was steeper for masculine-adjacent commercials. Moreover, themes stereotypically associated with masculinity paired more frequently with louder, distorted, and aggressive music, while stereotypically feminine themes with softer, calmer, and more harmonious soundtracks. Code and data to reproduce the results are available on github.com/marinelliluca/low-resource-RAG.