The AI doesn’t fix your data. It automates it. If your data is 40% stale, your AI will be 40% wrong.
Data quality is the #1 killer of AI projects. Not model selection. Not prompt engineering. Data.
Also see Data Layer (router) for quick navigation from concept and failure pages.
Key Concepts
- RAG — Retrieval-Augmented Generation: the pattern that makes AI useful
- Vector Databases — Where the knowledge lives
- Data Quality Failure — Garbage in, garbage out
- Knowledge Base Decay — The invisible rot
Core Technical Layer
- Embeddings — Converting text to searchable vectors; model choice determines what your RAG system can find
- Fine-Tuning — When to update model weights instead of the knowledge base; most SMBs need RAG, not fine-tuning
Quality and Maintenance
- Langfuse — Track retrieval quality over time; flags when knowledge base health drops
- Ollama — Runs embedding models locally for air-gapped RAG pipelines where data can’t leave the network
Data quality is the #1 killer of AI projects. Not model selection. Not prompts. Data. The AI doesn’t fix your data — it automates it.