Learning path · Embeddings & Representation · 42
Multimodal Embeddings
Joint vector spaces for text, images, audio, or video—enabling cross-modal search and retrieval.
Why it matters
- Powers image+caption knowledge bases and visual support tools.
- Requires different eval metrics than text-only RAG.
- Storage and pipeline complexity increase.
Key ideas
- Cross-modal similarity
- Unified index
- Modality-specific encoders
Multimodal embeddings align photos, slides, and descriptive text in one searchable space—"find diagrams like this failure mode." Index pipelines must extract alt text, OCR, and transcripts consistently. Retrieval evals need human judgment on visual relevance. Often combine generative multimodal models for answer synthesis with specialized embedders for search. Align legal review for visual assets with text corpora—screenshots may contain sensitive UI data text-only pipelines never saw. Ship only after eval gates pass on representative production failures.
Updated 2026-08-09 · Full learning path