Embeddings, Honestly
What Vectors Do and Don't Know
A book by Alpesh Nakrani
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- Pages
- 219
- Chapters
- 28
- Format
- PDF · 36.0 MB
What's in the book
Embeddings can make fuzzy language computable, power semantic search, cluster messy information, retrieve context for LLMs, and support recommendations. They do not know truth, authority, chronology, identity, permissions, freshness, or business rules. This research-backed edition works through the HONEST framework, retrieval architecture, metadata, reranking, evaluation, security, cost, model choice, and production anti-patterns for teams building semantic systems that must survive contact with real users.
A full field manual on what embeddings capture, what they forget, and why similarity is not truth.
Table of contents
PDF page- PREPreface: What Vectors Do and Don't Know—
- 01A Vector Is a Shadow15
- 02What Similarity Can Do22
- 03What Similarity Cannot Know29
- 04Similar Is Not Correct36
- 05The Numbers Are Not Labels43
- 06Distance Is a Ranking, Not a Verdict50
- 07The Map Is Learned, Not Universal57
- 08Dense, Sparse, and Hybrid64
- 09Semantic Search From First Principles71
- 10Chunking Is Where Retrieval Is Won78
- 11Metadata Is Reality86
- 12Vector Databases Are Not Magic93
- 13RAG Still Needs Judgment100
- 14Rerank Before You Believe107
- 15Evaluate or Guess114
- 16The Failure Modes Nobody Shows121
- 17Hybrid Search in Production128
- 18Graphs Remember Relationships135
- 19Multimodal Embeddings143
- 20Personalization and Recommendations150
- 21Embeddings for Code157
- 22Security Starts Before Retrieval164
- 23The Cost of Meaning171
- 24Choosing an Embedding Model179
- 25The Honest Retrieval Architecture186
- 26Use Case Playbook194
- 27The Embedding Anti-Patterns201
- 28The Honest Checklist Before Shipping208
- AAppendix A: Source Map215
The full chapters, illustrations, and reference material are included in the downloadable PDF.
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