Workflow
RAG Content Ingestion Pipeline
Convert messy docs into searchable, cited knowledge chunks for AI systems.
Problem
RAG quality collapses when source documents are duplicated, stale, poorly chunked, or missing metadata.
Solution
Build a repeatable ingestion workflow that cleans, chunks, embeds, labels, and refreshes sources before retrieval.
Steps
- 01Collect sources and assign canonical ownership.
- 02Remove duplicates, outdated files, and low-quality drafts.
- 03Chunk by semantic section with source URL, owner, date, and permissions.
- 04Embed chunks into a vector database and run retrieval tests.
- 05Schedule refreshes and flag stale content automatically.
Tools Used
Prompts Used
Variations
- Separate public docs from internal-only knowledge.
- Add a content-owner approval queue.
Related Dictionary
↳ connected nodes
Dictionary↳ linked
RAG (Retrieval-Augmented Generation)
Inject external knowledge into an LLM at query time.
Dictionary↳ linked
Semantic Search
Finding information by meaning rather than exact keyword match.
Dictionary↳ linked
Vector Database
A database optimized for similarity search over embeddings.
Dictionary↳ linked
Structured Output
Forcing AI responses into predictable schemas that software can use.
Tool Stack↳ linked
RAG Starter Stack
Minimum viable stack to ship a production RAG chatbot.
Tool Stack↳ linked
Knowledge Graph Stack
Relationship layer that maps concepts, workflows, prompts, tools, and cases.
Prompt↳ linked
Grounded Answer Prompt
Force the model to answer only from provided sources, with citations.
Comparison↳ linked
Vector Database vs Knowledge Graph
Similarity retrieval versus explicit relationship mapping.
Use Case↳ linked
Support Team Replaces Wiki Sprawl With a Knowledge Graph
A support org connected policies, playbooks, tickets, and RAG answers into one system.