---
id: wiki-2026-0508-asset-specific-knowledge
title: Asset-Specific Knowledge
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [자산 특정적 지식, tacit knowledge, institutional knowledge, tribal knowledge, moat, RAG]
duplicate_of: none
source_trust_level: B
confidence_score: 0.85
verification_status: applied
tags: [knowledge-management, tacit-knowledge, moat, rag, institutional, onboarding, fine-tuning]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: knowledge management
  applicable_to: [Custom RAG, Fine-tuning, Onboarding, Documentation]
---

# Asset-Specific Knowledge

## 📌 한 줄 통찰
> **"매 다른 곳 X 의 나만의 무기"**. 매 codebase / business / domain 의 deep context. 매 tacit (문서 X) + 매 high replacement cost. 매 modern AI 시대 의 가장 큰 differentiator — 매 generic LLM 의 X 가, 매 RAG / fine-tune 의 internalize.

## 📖 핵심

### 매 정의 (Williamson 1985)
- 매 specific asset 에 의 가치 의 lock-in.
- 매 site / physical / human / dedicated.
- 매 transfer cost 의 high.

### 매 type
1. **Tacit (암묵지)**: 매 doc X — 매 experience.
2. **Codebase**: 매 specific architecture / convention.
3. **Business domain**: 매 customer pattern / regulation.
4. **Process**: 매 workflow / decision rule.
5. **Relationship**: 매 customer / vendor.
6. **Historical**: 매 past incident / decision.

### 매 examples
- **War story**: "매 last year 의 deploy 의 X 의 fail 의 이유는..."
- **Convention**: "매 우리 team 의 매 React 의 hook 의 이런 식으로..."
- **Customer quirk**: "매 client A 의 매 Friday 의 deploy 의 X."
- **Performance**: "매 query X 의 매 prod 의 매 slow."
- **Regulation**: "매 our market 의 매 GDPR 의 매 X 적용."

### 매 Williamson 의 economics
- 매 transaction cost.
- 매 hold-up problem.
- 매 vertical integration.
- 매 firm boundary.

→ 매 economic moat 의 source.

### 매 challenges
1. **Bus factor**: 매 1 person → leave → 매 collapse.
2. **Onboarding**: 매 6 month + 매 mentorship.
3. **Documentation**: 매 stale.
4. **Knowledge transfer**: 매 hard.
5. **Tribal cliques**: 매 inclusion 의 X.

### 매 modern AI 적용

#### Custom RAG
- 매 internal docs + 매 LLM.
- 매 retrieval 의 specificity.
- 매 GPT 의 generic 의 enhance.

#### Fine-tuning
- 매 organization-specific data.
- 매 LoRA / QLoRA.
- 매 cost-effective.

#### Internal LLM
- 매 self-hosted.
- 매 data privacy.
- 매 brand voice.

#### Agent specialization
- 매 internal tool API.
- 매 codebase-specific guideline.
- 매 history-aware.

### 매 capture method
1. **Pair programming / shadowing**.
2. **Recorded sessions** (Loom, Tella).
3. **ADR / RFC**.
4. **Postmortem**.
5. **War story doc**.
6. **AMA / office hours**.
7. **LLM-mediated extraction** (interview → structured).
8. **Code comments** (선별적).

## 💻 패턴

### Internal RAG
```python
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter

# 매 internal sources
sources = [
    'wiki/*.md',
    'adr/*.md',
    'postmortem/*.md',
    'codebase/README.md',
    'slack/threads.json',  # 매 sanitized
]

splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
docs = []
for source in sources:
    for doc in load(source):
        docs.extend(splitter.split_documents([doc]))

vectorstore = Chroma.from_documents(
    docs,
    embedding=OpenAIEmbeddings(),
    persist_directory='./internal_kb',
)

def ask_internal(question):
    relevant = vectorstore.similarity_search(question, k=5)
    context = '\n\n'.join(d.page_content for d in relevant)
    return llm.generate(f"""Use this internal knowledge:
{context}

Question: {question}
Answer with citations to the source docs.""")
```

### Bus-factor mitigation
```python
def bus_factor_audit(repo):
    blame_data = get_git_blame_stats(repo)
    
    high_risk = []
    for file_path, contributors in blame_data.items():
        if not contributors: continue
        top_share = contributors[0].lines / sum(c.lines for c in contributors)
        if top_share > 0.8 and len(contributors) <= 2:
            high_risk.append({
                'file': file_path,
                'owner': contributors[0].name,
                'share': top_share,
            })
    return sorted(high_risk, key=lambda x: -x['share'])
```

→ 매 high bus-factor file 의 pair programming target.

### War story extraction (LLM-mediated)
```python
def extract_war_story(slack_thread):
    prompt = f"""Extract a structured "war story" from this Slack incident thread.

Format:
- Trigger: what initially failed
- Diagnosis: how it was identified
- Fix: what resolved it
- Lesson: non-obvious learning
- Tags: [domain, tech]

Thread:
{slack_thread}"""
    return structured_llm.generate(prompt)
```

### LoRA fine-tune (organization)
```python
from peft import LoraConfig, get_peft_model
from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer

base = AutoModelForCausalLM.from_pretrained('meta-llama/Llama-3-8B')
tokenizer = AutoTokenizer.from_pretrained('meta-llama/Llama-3-8B')

lora = LoraConfig(r=16, lora_alpha=32, target_modules=['q_proj', 'v_proj'], lora_dropout=0.05)
model = get_peft_model(base, lora)

# 매 org-specific Q&A pairs
trainer = SFTTrainer(model=model, train_dataset=org_dataset, tokenizer=tokenizer)
trainer.train()

# 매 tiny LoRA adapter (50MB) — 매 portable, 매 swappable.
```

### Onboarding doc template
```markdown
# [Service Name] Onboarding

## 30-second pitch
[1 sentence]

## Why it exists
[origin story + alternative considered]

## Key concepts
- [domain term 1]: meaning + when to use
- [domain term 2]: ...

## Common pitfalls
- [war story 1]: don't do X because Y happened
- [war story 2]: ...

## Who to ask
- [Topic A]: @person
- [Topic B]: @team

## Reading list (priority)
1. [link] — 30min
2. [link] — 1h
```

## 🤔 결정 기준
| 상황 | Strategy |
|---|---|
| Generic question | Public LLM |
| Codebase-specific | Internal RAG |
| Domain expert simulation | Fine-tune |
| Privacy-critical | Self-hosted LLM |
| Bus-factor risk | Pair programming + record |
| Onboarding | RAG + structured doc |
| War story | LLM extract + curate |

**기본값**: Internal RAG 의 baseline. 매 high-volume specific = LoRA. 매 critical = self-host.

## 🔗 Graph
- 변형: [[Tacit-Knowledge]] · [[Tribal-Knowledge]]
- 응용: [[RAG]] · [[Fine-Tuning]] · [[LoRA]] · [[Onboarding]] · [[ADR]]
- Adjacent: [[Postmortem]] · [[Moat]]

## 🤖 LLM 활용
**언제**: 매 internal RAG 설계. 매 onboarding system. 매 war story 의 extract. 매 organization-specific tool.
**언제 X**: 매 generic / public knowledge. 매 single-person consumption.

## ❌ 안티패턴
- **No documentation**: 매 leave → 매 collapse.
- **All in one head**: 매 bus factor 1.
- **Generic LLM 의 internal task**: 매 hallucination.
- **RAG 의 stale**: 매 outdated.
- **Fine-tune 의 small data**: 매 overfit.
- **No audit / curation**: 매 outdated war story.
- **Tribal exclusion**: 매 newcomer 의 onboard X.

## 🧪 검증 / 중복
- Verified (Williamson 1985, Polanyi tacit knowledge).
- 신뢰도 B.
- Related: [[RAG]] · [[Fine-Tuning]] · [[Onboarding]] · [[ADR]] · [[Bus-Factor]].

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — type + capture method + 매 RAG / LoRA / bus factor code |
