rag medium complexity mvp

Embedding

Embeddings turn text or other data into vectors so similar items can be compared mathematically.

Decision

Use embeddings when semantic similarity matters more than exact keyword matching.

Use when

Semantic search
Document clustering
Similar question matching
Retrieval candidate generation

Avoid when

Exact identifiers
Numeric calculations
Permission checks
Cases where keyword matching is enough

Why embeddings matter

Embeddings make semantic search possible. They let a system connect “refund policy” with “how do I get my money back” even when the exact words differ.

In most RAG systems, embeddings are used to find candidate chunks before the model answers. They are a retrieval tool, not a truth engine.

When exact search is better

If users search for IDs, codes, names, dates, or exact phrases, plain full-text search can be stronger. Many production systems use hybrid search because semantic and keyword signals catch different failures.

Common mistakes

Assuming embeddings understand permissions or business rules.
Using only semantic similarity for exact lookup.
Never testing different chunk sizes.

Next decision

Pair embeddings with a vector database when you need scalable similarity search. Add hybrid search when exact terms matter.