A traditional wiki stores information. A RAG system retrieves context dynamically.

Both solve knowledge access, but with different trade-offs.

AI Wiki strengths

• Stable editorial workflows
• Clear ownership and approval models
• Strong discoverability for humans

RAG strengths

• Context assembled at query time
• Better performance on long-tail questions
• Easier integration with operational data sources

Where teams get stuck

Many teams try to replace the wiki entirely with RAG. In practice, hybrid models work better:

• Keep canonical policy/process docs in a wiki.
• Use RAG for synthesis and workflow-level answers.
• Add source citation and freshness indicators.

Key design principle

Treat context as a product. The ingestion, retrieval quality, and governance model matter as much as model choice.

References

2. Is RAG Still Needed? Choosing the Best Approach for LLMs- Martin Keen - Master Inventor at IBM
3. How RAG, GraphRAG, and Context Engineering Improve AI Performance- Martin Keen - Master Inventor at IBM