The biggest limitation of an LLM is simple , it doesn’t know your data.
That’s where RAG (Retrieval Augmented Generation) becomes powerful.
Instead of retraining the model, we:
🔹 Store enterprise data in Delta Lake
🔹 Convert it into embeddings
🔹 Use Vector Search to retrieve the right context
🔹 Send that context to the LLM for grounded answers
With Databricks, this entire flow sits in one governed and scalable ecosystem making GenAI production-ready, not just a demo.
Why this matters:
RAG is behind today’s AI copilots, knowledge assistants, and enterprise chatbots.
Learning this means you’re building real-world AI systems.
Key skills to start:
Data prep • Embeddings • Vector search • Prompt orchestration • Model serving
Fine-tuning makes models smarter.
RAG makes them useful.
🌐 www.boopeshvikram.com
📺 https://www.youtube.com/@Beyoondboundaries
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