{
  "id": 7438327,
  "title": "30 Domain Templates for Your LLM Knowledge Base: Skip the Setup, Start Capturing",
  "url": "https://urgent.news/2026/09/15/30-domain-templates-for-your-llm-knowledge-base-skip-the-setup-start",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-15T01:17:15.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/paul_chen_90371fe7426cb44/30-domain-templates-for-your-llm-knowledge-base-skip-the-setup-start-capturing-3g2e"
  },
  "original_language": "en",
  "account": "Creating a domain-specific knowledge base for a large language model (LLM) can be complicated. The biggest hurdle isn't setting up the LLM itself, it's designing the knowledge base to reflect how knowledge is organized and accessed within that specific domain. Compliance analysts, for example, need every obligation mapped to the relevant regulation, control mapped to an owner, and exceptions mapped to an approver. A clinical wiki requires evidence grades on each claim, while a DevOps runbook system needs incident timelines and rollback steps. Query routing, intake form fields, and quality standards all differ between domains and aren't pre-configured.\n\nMost teams waste the first two weeks not capturing knowledge, but debating structure - which pages to create, how the system should route queries, which source types should have their own forms. By the time these questions are resolved, content has already been ingested in the wrong format and needs to be redone.\n\nThe synthadoc install --template command simplifies this process. It creates a structural skeleton for a specific knowledge domain in one command. The template provides six structural components:\n\n1. Query routing table (ROUTING.md) - tells the query engine which parts of the wiki to search based on the type of question. A finance template routes capital structure questions to debt financing pages, while routing valuation questions to financial models.\n2. Domain-tuned agent guidelines - AI coding assistant files (CLAUDE.md, AGENTS.md, GEMINI.md) are pre-populated with domain-specific instructions for tools like Claude Code, Gemini CLI, and GitHub Copilot Agents.\n3. Scaffold stub pages - empty frames with the correct titles, frontmatter, and markers to separate user-written content from automatically updated content.\n4. Blank intake forms - structured markdown forms in raw_sources/ for common document types within the domain.\n5. Pre-validated seed sources - curated ingest commands for public-domain reference sources, checked for accessibility and domain appropriateness.\n6. Scheduled maintenance jobs - a weekly lint run and scaffold job, pre-registered at install time and requiring no manual intervention.\n\nThere are 30 domain templates covering 9 categories, with finance, healthcare, and legal being the most common. The process of getting a domain-template wiki up and running can be completed in less than an hour. After installing the template, seed sources are ingested, and documents are batch ingested. The system then reviews candidate pages in Obsidian before they enter the live wiki.",
  "summary": "The hardest part isn't the LLM layer The hardest part of building an LLM knowledge base for a specific domain isn't wiring up the models, it's encoding how that domain thinks. A compliance analyst's knowledge base needs every obligation mapped to a specific regulation, every control to an owner, every exception to an approver. A clinical wiki needs evidence grades on every claim. A DevOps runbook…",
  "key_points": [
    "Simplifies domain-specific knowledge base creation for LLMs",
    "Includes query routing table, agent guidelines, scaffold stub pages",
    "30 templates available for finance, healthcare, legal and more categories"
  ],
  "editors_take": "The availability of 30 domain templates for large language model knowledge bases streamlines setup by providing pre-configured structures, allowing teams to focus on capturing knowledge rather than debating structure.",
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}