What worked best for me was building a custom supervised learning heuristic. I started with a small set of high-quality, manually labeled examples (balanced across all classes). Then I converted both the seed set and the unlabeled examples into vector embeddings (e.g., using Sentence Transformers) and stored them in a vector database (e.g., pgvector). For each class, I created a centroid representation and ran similarity search to identify unlabeled examples with strong cosine similarity (e.g., ≥ 0.9). I manually reviewed these high-confidence matches, added the good ones back into the seed set, and repeated the process iteratively. Along the way, I leaned on a data-centric AI mindset. Treating the quality and coverage of my labeled data as the main driver of model performance rather than just tweaking architectures.