Contextual embeddings incorporate surrounding context to generate more accurate document representations. Problem: Standard chunking embeds each chunk in isolation, losing document-level and positional context. A chunk about "the process" may be ambiguous without knowing what document it's from. Solutions: Prepend context: Add document title, section headers, or summary to each chunk before embedding. Contextual embedding models: Train embeddings that consider surrounding text. Late contextualization: Retrieve chunks, inject parent context at generation time. Implementation: For each chunk, prepend "Document: {title}. Section: {section}. Content: {chunk}" then embed. Anthropic's approach: Prepend LLM-generated chunk summary that situates the chunk in document context. Benefits: Resolves ambiguous references, improves retrieval relevance, particularly for structured documents. Trade-offs: Longer text to embed (cost, potential truncation), preprocessing overhead. Use cases: Technical documentation with sections, legal documents, any content with document-level context. Results: Significant retrieval improvements (20-30% on some benchmarks), especially for out-of-context chunks.