Automation consultant. I build AI-powered workflows using Claude, n8n, and open-source tools. Sharing practical guides on AI agents, no-code automation, and cost optimization.
The bank/bank disambiguation problem is a great framing. One approach that's worked well for me in production RAG: store two embeddings per chunk — one from a general model, one from a domain-specific fine-tuned model — and use a reranker to fuse scores. The financial model will correctly distance "river bank" from "savings account" even when the general model confuses them.
Adding entity-typed metadata (extracted at ingestion with an NER pass) also helps — you can filter to entity_type=FINANCIAL_INSTITUTION before the vector search even runs.
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The bank/bank disambiguation problem is a great framing. One approach that's worked well for me in production RAG: store two embeddings per chunk — one from a general model, one from a domain-specific fine-tuned model — and use a reranker to fuse scores. The financial model will correctly distance "river bank" from "savings account" even when the general model confuses them.
Adding entity-typed metadata (extracted at ingestion with an NER pass) also helps — you can filter to
entity_type=FINANCIAL_INSTITUTIONbefore the vector search even runs.