Text Embeddings

Text embeddings represent words, sentences, or passages as arrays of numbers generated by a machine learning model. Their usefulness comes from the relationships between vectors: text with related meaning tends to appear close together under an appropriate similarity measure. For example, a question about “recovering access to an account” may match a document titled “Password reset procedure” without sharing the same keywords. Embeddings capture learned patterns, not guaranteed understanding or factual correctness.

In cloud applications, an ingestion pipeline typically extracts text from documents, divides it into manageable chunks, and sends those chunks to an embedding model. It stores the resulting vectors in a searchable index alongside the source text or references to it, plus metadata such as document version, department, and access permissions. At query time, the application embeds the user's question and searches for similar document vectors. Cosine similarity, dot product, and Euclidean distance are common comparison methods; the correct choice depends on the model and index configuration.

Retrieval augmented generation is an application pattern that retrieves relevant information from an external source and supplies it as context to a generative model. Embeddings commonly power the retrieval step, although RAG can also use keyword search, structured queries, or hybrid retrieval. The retrieved passages, rather than the vectors themselves, usually enter the generation prompt. A service such as Amazon Bedrock Knowledge Bases can orchestrate document ingestion, embedding generation, retrieval, and integration with answer generation.

Typical business applications include employee policy assistants, customer support systems, technical documentation search, and research tools. An employee might ask whether a particular travel expense is reimbursable. A RAG application retrieves passages from the current expense policy and asks a foundation model to answer using that evidence, ideally with source citations. Updating the indexed policy can change the available evidence without retraining the generative model. RAG does not modify the model's weights and is not the same as fine-tuning.

Chunking is an important design decision. Very small chunks may lose qualifications or surrounding context, while very large chunks can mix unrelated subjects and consume more prompt tokens. Overlap can preserve context across boundaries but increases storage and processing. Document structure, language, domain terminology, and embedding model input limits also affect retrieval quality. Evaluate representative questions against expected source passages rather than selecting a model solely by vector size.

Document and query vectors must use a compatible embedding space, normally provided by the same model and version with the appropriate document and query settings. Equal vector dimensions do not make vectors from different models comparable. Changing the embedding model generally requires re-embedding the corpus and rebuilding or migrating the index. Higher-dimensional vectors can increase storage and search costs without necessarily improving results for a particular workload.

Vector similarity is not a probability that a passage is correct or sufficient. Approximate nearest-neighbor indexes trade some retrieval recall for speed, and semantic matching can miss exact identifiers, dates, or product codes. Hybrid keyword and vector search, metadata filters, and reranking can improve relevance. RAG can reduce unsupported answers but does not eliminate hallucinations or malicious instructions embedded in retrieved documents. Apply authorization during retrieval, treat embeddings as potentially sensitive data rather than anonymized text, propagate document updates and deletions, and measure both retrieval quality and answer grounding.