Embeddings
Dense representations, model coupling and the migration they imply.
3 to work through
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beginner Multiple choice
A support search drops any result scoring below 0.75 cosine similarity. The team re-embeds the whole corpus with a newer embedding model and rebuilds the index; now the same cutoff throws away almost every result. What is the mistake?
3 min answer -
advanced
A marketplace uses embeddings for product similarity. The embedding model is upgraded. What is the operational consequence, and how should the system be designed for it?
2 min answer -
advanced
You are building retrieval over an organisation's documents. Which three decisions most affect quality?
2 min answer
3 terms in this topic
Embedding Model Migration
The process of moving a corpus to a new embedding model, which requires re-embedding everything because vectors from different models are not comparable.
conceptEmbedding Table Collision
Two unrelated identifiers mapped to the same row of a fixed-size embedding table, so their learned representations are averaged together and the mode…
conceptEmbeddings
Dense numeric representations of content that place similar things close together — the substrate of semantic search and retrieval.
Neighbouring topics
AI-Era Architecture
General material on architecting systems that include models.
LLM Application Architecture
The shape of a production system with a model in the request path.
RAG Architecture
Retrieval, grounding, citation and the permissions RAG can enforce.
Vector Databases
Approximate nearest-neighbour search, filtering and re-indexing.
Chunking & Retrieval
Structure-aware splitting, hybrid search and why chunking dominates quality.
Reranking
Cross-encoders improving precision more than a bigger embedding model.
Model Selection
Capability, latency, cost and the evaluation that decides between them.
AI Gateways
Centralised routing, keys, quotas, caching, logging and safety policy.
Prompt & Version Management
Prompts as reviewed, versioned, evaluated production configuration.
Agent Architectures
Loops, planning, memory and the boundaries an agent must not cross.
Tool Calling
Typed tool interfaces, narrow parameters and per-tool authorisation.
Multi-Agent Systems
Coordination, hand-off and whether more agents actually help.
LLM Evaluation
Held-out sets, rubric judging, CI gates and production sampling.
AI Observability
Logging prompts, versions, retrieved context and cost per request.
Guardrails
Deterministic checks on input and output that fail closed.
Prompt Injection Defence
Breaking the private-data, untrusted-input, outbound-channel combination.
AI Cost Management
Token accounting, routing, caching and the context-window budget.
Human in the Loop
Gating by reversibility and blast radius, and avoiding approval fatigue.
ML Platform
Feature stores, training pipelines, registries and deployment.