# RAG Knowledge Platform

URL: https://justrunme.com/cases/rag-knowledge-platform/
Author: Andrey Lesnikov
Role: Senior Infrastructure & DevOps Engineer, CONTACT Software GmbH
Topics: AI infrastructure, Kubernetes, GitOps, Terraform, observability, platform engineering

A RAG knowledge platform turns repositories, runbooks, architecture notes, and project metadata into retrievable engineering context with citations and safer answer boundaries.

## Snapshot

- Problem: Engineering knowledge is spread across repositories, runbooks, tickets, architecture notes, and project history.
- Constraints: Source freshness, citation quality, chunking, access boundaries, hallucination control, and explainable answers.
- Architecture: Curated ingestion pipeline with markdown exports, project metadata, embedding-ready documents, source references, and fallback local answers.
- Result: The AI assistant can answer infrastructure questions with project context, sources, and a safer boundary around what it knows.

## Architecture Notes

### Problem

Engineering knowledge usually lives across GitHub repositories, runbooks, tickets, docs, and chat history. A generic AI assistant cannot answer infrastructure questions reliably without curated context and source references.

### Architecture

The platform exports structured markdown, project metadata, case studies, and recognition data into retrieval-ready documents. Answers reference known sources, fall back to curated local knowledge when APIs are offline, and avoid pretending live counters are audited production facts.

### Trade-offs

RAG quality depends on curation, chunking, freshness, and access boundaries. A smaller high-quality knowledge base is more useful than dumping every private note into embeddings.

### Result

The AI Twin can answer platform questions with context, cite public project signals, and support conference conversations without needing every visitor to inspect the full site.

## Architecture Diagram

Repos + runbooks -> Curated markdown -> Embeddings/RAG -> AI Twin -> Cited answer

## Constraints

- Keep source freshness and citations visible so answers can be trusted and checked.
- Avoid indexing private or low-quality context that would make answers noisy or unsafe.
- Provide useful local fallback behavior when external AI APIs are unavailable.

## Key Decisions

- Export curated profile, project, recognition, and case-study data as markdown and structured metadata.
- Prefer smaller high-signal source sets over dumping every document into retrieval.
- Show sources in the UI so the assistant behaves like a technical conversation surface, not a generic chatbot.

## Failure Modes

- Stale chunks cause confident answers based on old architecture decisions.
- Missing citations make AI output hard to verify in engineering conversations.
- Overbroad ingestion mixes public portfolio data with internal or irrelevant content.

## Result

The AI Twin can answer infrastructure questions with scoped project context, source references, and clear knowledge boundaries.

## Related Technologies

- RAG
- Embeddings
- Markdown exports
- Node API
- OpenAI API
- GitHub metadata
- Structured data
- AI Twin


## FAQ

### What should be indexed in a RAG platform for DevOps work?

Architecture cases, runbooks, repository READMEs, operational notes, diagrams, deployment contracts, incident lessons, and public project metadata.

### How do you reduce hallucinations in a RAG assistant?

Use curated sources, short factual chunks, citations, freshness signals, fallback answers, and explicit boundaries about what is simulated or not audited.

## Related URLs

- Case index: https://justrunme.com/cases/
- AI infrastructure hub: https://justrunme.com/ai-infrastructure/
- Kubernetes GitOps hub: https://justrunme.com/kubernetes-gitops/
- Profile: https://justrunme.com/