Files
2nd/10_Wiki/Topics/Domain_Programming/AI_and_ML/Connect-AI-Documentation.md
T
Antigravity Agent c24165b8bc refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게
[공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류.
문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인).

- Topic_Programming → Domain_Programming (내부 구조 보존)
- Topic_Graphic → Domain_Design
- Topic_Business → Domain_Product
- Topic_General → Domain_General
- _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning),
  Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing)
- 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서)
- 빈 폴더 정리 (memory/procedures)
- 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-11 11:05:56 +09:00

3.7 KiB

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack
id title category status canonical_id aliases duplicate_of source_trust_level confidence_score verification_status tags raw_sources last_reinforced github_commit tech_stack
wiki-2026-0508-connect-ai-documentation Connect AI Documentation 10_Wiki/Topics verified self
ConnectAI documentation
AI agent documentation
system docs
SOP
API spec
none B 0.83 applied
documentation
ai-agent
sop
api-spec
knowledge-base
project-internal
2026-05-10 pending
language applicable_to
documentation
Internal AI Agent Project
SOP
Onboarding

Connect AI Documentation

매 한 줄

"매 complex AI 의 readable 의 manual". 매 system architecture + SOP + API spec 의 unified. 매 modern: 매 living doc + 매 RAG 의 query-able + 매 AI 의 self-update. 매 Codebase_Onboarding_Guide 와 의 align.

매 핵심 component

  1. System Architecture: 매 C4 model.
  2. SOP: 매 step-by-step operation.
  3. API Spec: 매 OpenAPI / GraphQL schema.
  4. ADR: 매 decision record.
  5. Glossary: 매 term.
  6. Runbook: 매 incident response.
  7. Onboarding: 매 new dev.

매 living doc 의 element

  • CI 의 sync: 매 stale 의 detect.
  • RAG-queryable: 매 LLM 의 answer.
  • Code → doc: 매 generate from source.
  • Multi-format: markdown + diagram + interactive.

매 best practice

  • 매 README first.
  • 매 Diátaxis (tutorial / how-to / reference / explanation).
  • 매 minimum viable doc.
  • 매 example > prose.
  • 매 versioning.
  • 매 link > duplicate.

💻 패턴

Doc-as-code (Mintlify / Docusaurus / Astro)

docs/
├── index.mdx
├── architecture.mdx (C4)
├── runbook/
│   ├── incident-database.mdx
│   └── incident-deploy.mdx
├── api/
│   └── (OpenAPI generated)
└── onboarding.mdx

Living doc (CI sync check)

- name: Doc drift check
  run: |
    npm run extract-types
    git diff --exit-code docs/api-types.json || \
      (echo "Doc out of sync — regenerate" && exit 1)

RAG over docs

from langchain.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter

splitter = RecursiveCharacterTextSplitter(chunk_size=1000)
chunks = splitter.split_documents(load_md_files('docs/'))
vectordb = Chroma.from_documents(chunks, embeddings, persist_directory='./doc-rag')

def ask_doc(question):
    relevant = vectordb.similarity_search(question, k=5)
    return llm.generate(f"Use these docs:\n{relevant}\n\nQuestion: {question}")

OpenAPI generation

// 매 zod-to-openapi
import { extendZodWithOpenApi } from '@asteasolutions/zod-to-openapi';
extendZodWithOpenApi(z);

const userSchema = z.object({
  id: z.string().openapi({ example: 'uuid-...' }),
  email: z.string().email().openapi({ example: 'user@example.com' }),
}).openapi('User');

🔗 Graph

🤖 LLM 활용

언제: 매 internal AI project 의 doc design. 언제 X: 매 single-file script.

안티패턴

  • Stale doc: 매 CI 의 sync 의 X.
  • Single huge file: 매 navigate 의 X.
  • No examples: 매 pure prose.
  • Code 의 doc 의 mismatch: 매 source-of-truth 의 ambiguous.

🧪 검증 / 중복

🕓 Changelog

날짜 변경
2026-05-08 Phase 1
2026-05-10 Manual cleanup — components + 매 doc-as-code / RAG / OpenAPI code