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. A deployment can survive a mediocre model — it cannot survive a rotten knowledge base. Every RAG system is only as trustworthy as the documents behind it, and every fine-tuning effort is only as good as the labels fed into it.
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 with your private data
- Vector Databases — Where the knowledge lives, indexed by meaning
- Embeddings — Converting text to searchable vectors; the model you pick decides what retrieval can find
- Fine-Tuning — When to update model weights instead of the knowledge base; most SMBs need RAG, not fine-tuning
- Model Collapse — How training on AI-generated content degrades models over generations — a risk even for teams that never train
- Context Window Exhaustion — When prompt + retrieved context + history exceeds the model’s limit, and what breaks
Core Technical Layer
- Embeddings — The vector layer that makes semantic search possible
- Vector Databases — Storage and similarity search for those vectors
- RAG — The retrieval pattern that ties embeddings and vector stores to the LLM
- Fine-Tuning — The behavior-change path, distinct from the knowledge path
Quality and Maintenance
- Data Quality Failure — Garbage in, garbage out; the most common RAG failure
- Knowledge Base Decay — The invisible rot when nobody owns the knowledge base
- Eval Gap — Silent quality drift when teams ship without an evaluation suite
- Model Version Sprawl — Unmanaged model versions across dev, staging, and prod
Tools for This Layer
- Ollama — Runs embedding models locally for air-gapped RAG pipelines where data can’t leave the network
- Langfuse — Tracks retrieval quality over time; flags when knowledge base health drops
- Flowise — Low-code builder for RAG pipelines and multi-agent retrieval workflows
- LiteLLM — Routes between embedding and inference providers; useful when the data layer spans multiple models
Decision Guide
| Symptom | Likely Layer | Start Here |
|---|---|---|
| Model gives wrong facts about your product | Knowledge (RAG) | RAG |
| Retrieval misses obviously relevant docs | Embeddings | Embeddings |
| Model tone or format is wrong | Behavior (fine-tuning) | Fine-Tuning |
| Quality dropped silently since launch | Eval / monitoring | Eval Gap |
| Knowledge base answers going stale | Maintenance | Knowledge Base Decay |
| Costs spiking on long conversations | Context window | Context Window Exhaustion |
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.
Latest Updates
- June 2026 — Context Window Exhaustion
- June 2026 — Model Collapse
- June 2026 — Embeddings
- June 2026 — Fine-Tuning
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