Embeddings: Similarity Search, Not Summaries
Explore how embeddings transform text into vectors to perform similarity search, enabling AI workflows to retrieve exact policy excerpts. Understand why embeddings differ from summaries and how to maintain evidence-grounded retrieval for compliance and accuracy in support systems.
We'll cover the following...
The agent loop can now call tools safely, stay within its limits, and stop for a human when something’s unclear but every tool it has used so far returns structured facts like an account status or a template id. Support questions often need something else: the exact wording of a policy. A support ticket asks, Can I get a refund after 45 days? and the policy document contains a line that says Returns are allowed within 30 days of delivery. A naive keyword lookup for 45 days can miss that clause because the policy uses different words, or it can return a paragraph about 45 day shipping windows that happens to contain the same number.
The desired behavior in a RAG workflow is to retrieve the policy snippet that is closest in meaning to the question even when the wording differs. That retrieval step is what embeddings enable, because the system can compare the question and each policy chunk by similarity instead of exact token overlap. We focus ...