Embeddings

• You need semantic search over docs, tickets, or events.
• Building recommendation or deduplication features without labels.
• Optimizing RAG recall for domain-specific terminology.

Embedding choice affects recall, speed, and cost. PMs balance vector quality versus storage/compute costs and latency. Good embeddings reduce hallucinations by surfacing the right context; poor ones create noise and frustrate users. Versioning matters because changes can invalidate existing indexes.

Pick a model sized for your latency and domain; test multilingual if needed. Normalize text, remove boilerplate, and store metadata for filtering. Track embedding drift: re-index when you change models or preprocessing. In 2026, prefer cheaper domain-tuned small vectors plus re-ranking for precision.

Switching from a generic 1536-dim model to a domain-tuned 768-dim one cut storage 45% and improved top-3 recall on support tickets from 74% to 86%, dropping answer edits per ticket by 18%.

• Mixing embedding versions in the same index, hurting relevance.
• Skipping metadata filters, forcing the model to sift irrelevant chunks.
• Over-indexing huge boilerplate sections that drown out signal.