Beyond Search: 86.4% MMLU, 77.6 MTEB, and the New Architecture of Policy Understanding

Author(s): R. Thompson (PhD)

Originally published on Towards AI.

“Amidst the proliferation of generative technologies, the true constraint remains epistemic access — especially within public systems.”

The corpus of legal, regulatory, and policy documents maintained by governments and NGOs has grown into a dense, heterogeneous ecosystem. These documents, often drafted in domain-specific language and archived across disparate formats such as PDFs, scanned text, and fragmented HTML, pose a formidable barrier to access and interpretation. For administrators, legal personnel, and constituents, the task of retrieving pertinent clauses or aligning practices with current mandates is fraught with inefficiencies, ambiguity, and latency.

A 2019 report by McKinsey quantified the magnitude of this issue, noting that up to 30% of a public employee’s time is spent locating internal information. In domains governed by high regulatory volatility or compliance sensitivity, this inefficiency is not merely inconvenient — it is structurally incapacitating. The imperative for a cognitively intelligent interface between users and policy repositories is now self-evident.

This article introduces a systematized retrieval and reasoning framework: the Smart Policy Search Engine. Architected using LangChain’s composability, BGE-M3’s multilingual embedding prowess, ChromaDB’s high-dimensional indexing, and GPT-4’s generative fidelity, the engine serves as a neuro-symbolic bridge between unstructured regulatory texts and human queries.

Unlike conventional search systems that rely on keyword density… Read the full blog for free on Medium.

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Published via Towards AI