Gen AI data lifecycle
Gen AI systems improve through tight feedback loops, not one-off training runs. This page outlines a proven lifecycle for building reliable Gen AI pipelines.1. Ingest unstructured data
Start by centralizing all relevant sources:- Documents (PDFs, HTML, knowledge bases)
- Text datasets
- Audio transcripts
- Images and multimodal assets
- Metadata describing source, freshness, and trust
2. Curate for grounding and quality
Not all data should be used for retrieval or training. Curation focuses on:- Removing duplicates and low-signal data
- Identifying hallucination-prone sources
- Grouping content by domain or intent
- Selecting data for targeted evaluation
3. Annotate feedback and intent
Human feedback is central to Gen AI alignment:- Classification (correct / incorrect / unsafe)
- Ranking and preference selection
- Structured explanations
- Instruction-following evaluation
4. Evaluate model behavior
Evaluation should be continuous and comparative:- Prompt-level performance
- Dataset-level trends
- Model-to-model comparisons
- Regression detection
5. Close the feedback loop
Evaluation insights drive the next cycle:- Re-curate data
- Expand edge-case coverage
- Refine feedback schemas
- Update prompts or retrieval sources
Key takeaway
Reliable Gen AI is not a single model — it’s a living system:Curate → Evaluate → Feedback → Improve → Repeat