From Raw Text to Embeddings
Explain how tokenization and embeddings turn raw text into dense vectors for neural models.
The search puzzle A lawyer searches for early termination but wants clauses that say cancellation right, exit for convenience, and wind-down notice too. Exact-match search misses language that means similar things in context. Representation Text -> tokens -> vectors -> similarity The neural-network step is not magic understanding. It is a learned numerical representation that makes similarity searchable. Keyword Find exact words only Semantic retrieval with evidence checks. Embeddings are learned similarity, not proof. Retrieval should point you to source text, not replace review. 01 Represent 02 Retrieve 03 Verify Text to numbers The clause is a paragraph of legal…
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