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エンジニア向け研修

• AI Shift
  • 社内SQL研修のために作った資料を公開します - (2021/06/21)
• Classi
  • 当たり前にリリースしていく ~ 新卒研修編 - (2021/05/20)
  • リモートワークのための質問力向上研修を実施しました - (2021/12/07)
• CyberZ
  • 良いコードとは何か - エンジニア新卒研修 スライド公開 - (2021/04/27)
• DMM.com(旧DMM.comラボ含む)
• ＤＭＭ.comラボ16新卒エンジニア研修 - (2016/08/24)

oppara

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.