Why it matters

Custom RAG offers control + portability. Understanding shapes flexible LLM apps.

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The architecture

Embedding: call embed API (Vertex embeddings or others).

Vector search: query DB with query embedding.

Prompt augmentation: inject retrieved into agent context.

Custom RAG patternEmbed queryVertex or othersVector searchDB callInject into promptgrounded responseTrade custom infrastructure for portability + control
Custom RAG steps.
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How it works end to end

Vector DB choice: managed (Pinecone) or self-hosted (Milvus, pgvector).

Chunking strategy: fixed size, semantic, structural.

Reranking: cross-encoder for better precision after retrieval.

Metadata filtering: per-tenant scoping.