dev.toFactualityFactuality is a Premium featureCenterCenterBased on the outlet's overall lean; article framing check pending

Hi guys, I’ve spent the last couple of years building a legal AI platform. We’ve acquired several clients, including established law firms working on complex arbitration cases. And given the kind of documents these firms deal with, we’ve had to process some pretty challenging material. Along the way, we invested a significant amount of time hardening our stack: reliable document ingestion, retrieval, source-linked citations, long-running agents, and handling all sorts of unexpected user behaviours. What struck me is how much effort went into making the foundations reliable, before we could…

3 hours ago · dev.to · You are reading this one

dev.toFactualityFactuality is a Premium featureCenterCenterBased on the outlet's overall lean; article framing check pending

Short answer: for a legal contract review workspace, use a retrieval architecture with indexed contract text, live changing facts, and citations that preserve tenant and access metadata. For a legal contract review workspace built over a folder of PDFs, relevance tuning starts with an answer contract, not a vector database. The contract should say what may be retrieved, which source identifies it, and how a reviewer can open that source. Recall without access control is a data leak. Precision without a citation is just a confident-looking dead end. My default choice is a hybrid boundary…

3 hours ago · dev.to

dev.toFactualityFactuality is a Premium featureCenterCenterBased on the outlet's overall lean; article framing check pending

Short answer: use staged retrieval with explicit collections, bounded queries, and source context that can be traced back to the exact listing PDF revision. That answer is intentionally less exciting than choosing a vector index. It is more useful. In a fintech-adjacent property workflow, a user may ask, “Which listings allow a 30-day close?” The response needs evidence, not just a similarity score. Define the retrieval contract first, then price the lifecycle of that contract at scale. The field guide: choose a boundary before a database The clean boundary is: ingestion owns document…

22 hours ago · dev.to

dev.toFactualityFactuality is a Premium featureCenterCenterBased on the outlet's overall lean; article framing check pending

Short answer: use a staged retrieval design with explicit collections, bounded queries, and source context that can be traced back to the document a reader was allowed to see. That trade-off matters more than picking the fanciest vector index. In a property-management product, an answer about a lease-sync worker is only useful if the engineer can see which access rule admitted the source and which revision supplied the claim. Start with a retrieval contract Write the user-visible answer first, then define the contract that must produce it. For internal engineering documentation, my contract…

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