AI Developmentr/LocalLLaMA

I classified 3.5M US patents with Nemotron 9B on a single RTX 5090 — then built a free search engine on top

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ai-coding-toolslocal-llm-workflowsdomain-specific-aihybrid-search-architecture

“Patent attorneys need exact phrase matching. 'solid-state battery electrolyte' should match those exact words, not semantically similar documents about 'energy storage.' FTS5 gives sub-second queries on 3.5M records with zero external dependencies.”

Key takeaways

  • Local LLM (Nemotron 9B on RTX 5090) classified 3.5M patents in 48 hours, demonstrating consumer hardware can handle enterprise-scale classification tasks
  • Contrarian architecture choice: FTS5 full-text search outperforms vector embeddings for domain-specific use cases requiring exact phrase matching and deterministic results
  • Complete technical stack disclosed: SQLite FTS5 + local LLM query expansion + BM25 ranking with custom weights + FastAPI, hosted on Chromebook via Cloudflare Tunnel - proving production-grade search doesn't require cloud infrastructure
  • Patent lawyer with 1 month coding experience built production search engine, signaling democratization of AI tooling for domain experts without traditional engineering backgrounds
  • Hybrid approach: Uses LLM for natural language query expansion into boolean queries, then traditional search for retrieval - combining strengths of both paradigms

Why this matters for operators: Legal tech, enterprise search architecture, local LLM deployment for specialized domains, hybrid search strategies

I cover AI×GTM intelligence like this every Wednesday.

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This analysis was produced using the STEEPWORKS system — the same agents, skills, and knowledge architecture available in the GrowthOS package.