Overview
Databricks’ compilation of GenAI technical content provides a staged path from basic to advanced GenAI deployment, with use cases and code samples at each stage. The core thesis: achieving production-quality GenAI requires new tools and skills, and the quality of underlying data infrastructure is the determining factor.
Key Frameworks
The 6-Stage GenAI Deployment Path:
| Stage | Approach | Complexity | Use Case |
|---|---|---|---|
| 0 | Foundation Models | Low | Use off-the-shelf LLMs (DBRX, GPT-4) |
| 1 | Prompt Engineering | Low | Product review analysis, structured extraction |
| 2 | RAG | Medium | Knowledge-grounded Q&A with vector search |
| 3 | Fine-Tuning | Medium-High | Bespoke LLMs, LoRA for parameter efficiency |
| 4 | Pretraining | High | Training models from scratch (<$50K with MosaicML) |
| 5 | LLM Evaluation | Cross-cutting | RAG evaluation, offline assessment pipelines |
Data Lakehouse as GenAI Foundation: The data lakehouse architecture provides the unified platform for data preparation, model training, serving, and monitoring that GenAI applications require.
Actionable Takeaways
- Progress through stages sequentially — don’t jump to fine-tuning before exhausting prompt engineering and RAG
- LLM evaluation is the most underinvested area — build systematic evaluation pipelines early
- LoRA (Low-Rank Adaptation) makes fine-tuning accessible with dramatically reduced compute requirements
- Pretraining from scratch is viable at <$50K with the right infrastructure (MosaicML/Databricks)
- Data quality at every stage is the single biggest determinant of output quality
Related
- compact-guide-to-rag — deep dive into Stage 2 (RAG) of this deployment path
- pretraining-custom-llms — detailed treatment of Stages 3-4 (fine-tuning and pretraining)
- big-book-of-mlops — the MLOps foundation these GenAI pipelines run on
- ai-engineering-foundation-models — Chip Huyen’s complementary end-to-end framework