Results for “knowledge-bases”

Provides patterns to build Retrieval-Augmented Generation (RAG) systems for AI applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.

Build and organize knowledge bases, help centers, FAQ systems, and troubleshooting guides optimized for self-service support and search. Use when creating a structured support documentation system. Also trigger for 'knowledge base', 'help center', 'FAQ', 'troubleshooting guide', 'support docs', 'self-service support', 'help article', or 'support article'.

Build the operator knowledge base future-you and any AI can actually search. Capture facts, playbooks, stakeholders, and how-tos as plain greppable markdown, organized so the answer is a search away instead of a memory or a Slack scroll. Trigger on "start a knowledge base", "write this down somewhere findable", "document how we do this", "who owns what again", "capture this for later", or any fact you have now looked up more than once.

Search personal knowledge bases, notes, docs, and meeting transcripts locally using qmd — a hybrid retrieval engine with BM25, vector search, and LLM reranking. Supports CLI and MCP integration.

Manages a multi-layered knowledge system for ingesting, organizing, syncing, and retrieving knowledge across local files, MCP memory, vector stores, and Git repos.

Maps, documents, and onboards into an existing codebase by generating seven structured documents covering stack, structure, architecture, conventions, integrations, testing, and concerns.

Build a persistent, compounding knowledge base that combines a wiki, knowledge graph, and raw source storage for structured retrieval with provenance.

Karpathy's LLM Wiki: build/query interlinked markdown KB.

Karpathy's LLM Wiki: build/query interlinked markdown KB.

Search personal knowledge bases, notes, docs, and meeting transcripts locally using qmd — a hybrid retrieval engine with BM25, vector search, and LLM reranking. Supports CLI and MCP integration.

Build and maintain a persistent, interlinked markdown knowledge base that compiles knowledge once and keeps it current, with cross-references and contradiction flags.

Write clear, searchable help center articles and FAQ entries based on support data, product documentation, and common customer questions. Use when the user requests knowledge base article writing or provides relevant inputs for this workflow.

Karpathy's LLM Wiki: build/query interlinked markdown KB.

Build and maintain a personal knowledge base (wiki) using LLMs. Instead of RAG-style retrieval, the LLM incrementally compiles, cross-references, and maintains a persistent structured wiki from raw sources. Use when user wants to create a knowledge base, build a personal wiki, organize research notes, ingest documents into a structured wiki, or maintain a living knowledge repository.

Use to audit a help centre or knowledge base against the conversations customers actually have, finding coverage gaps, stale articles and unused content. Trigger for "what should we write help articles about", "audit our knowledge base", KB coverage gaps, self-service deflection content, or preparing a knowledge base to ground an AI support agent.

Manage a multi-layered knowledge system for ingesting, organizing, syncing, and retrieving knowledge across local files, MCP memory, vector stores, and Git repos.

Internal codebase knowledge-check assistant for maintainers. Focus on architecture understanding, implementation details, and change-risk analysis for this repository.

Karpathy's LLM Wiki — build and maintain a persistent, interlinked markdown knowledge base. Ingest sources, query compiled knowledge, and lint for consistency.

Maintains a PARA-structured knowledge base on disk for persistent project context, decisions, and architecture records across agent sessions.

Knowledge-base steward in the spirit of Niklas Luhmann's Zettelkasten. Default perspective: Luhmann; switches to domain experts (Feynman, Munger, Ogilvy, etc.) by task. Enforces atomic notes, connectivity, and validation loops. Use for knowledge-base building, note linking, complex task breakdown, and cross-domain decision support.

Personal knowledge base powered by Ensue for capturing and retrieving understanding. Use when user wants to save knowledge, recall what they know, manage their toolbox, or build on past learnings. Triggers on "save this", "remember", "what do I know about", "add to toolbox", "my notes on", "store this concept".

跨多个存储层（本地文件、MCP memory、向量存储、Git 仓库）的知识库管理、摄取、同步和检索。在用户想要保存、组织、同步、去重或跨知识系统搜索时使用。

Karpathy's LLM Wiki: build/query interlinked markdown KB.

Karpathy's LLM Wiki: build/query interlinked markdown KB.

Builds a personal knowledge base for capturing and retrieving understanding, with structured namespaces for concepts, tools, patterns, and references.

Karpathy's LLM Wiki: build/query interlinked markdown KB.

Karpathy's LLM Wiki — build and maintain a persistent, interlinked markdown knowledge base. Ingest sources, query compiled knowledge, and lint for consistency.

Capture conversations and decisions into structured Notion pages for easy reuse as wiki entries, how-tos, decisions, or FAQs with proper linking.

Query the bundled research knowledge graph for methodology guidance. Routes questions through a 3-tier knowledge base — WHY (research claims), HOW (guidance docs), WHAT IT LOOKS LIKE (domain examples) — plus structured reference documents. Returns research-backed answers grounded in specific claims with practical application to the user's system. Triggers on "/ask", "/ask [question]", "why does my system...", "how should I...".

Persists and recalls agent knowledge across sessions using four approaches: static KB, company wiki, persistent memory, and session brain.