LLM PoC on Databricks
Validate your LLM use case on Databricks
What the solution delivers for your organisation
Increase developer productivity
The PoC delivers a working pipeline your team can immediately extend, rather than starting from scratch after validation.
Easily monitor performance
Monitoring is included, not a later addition. You see how the model performs on your data from day one.
Reduce total cost of ownership
Validating on your existing Databricks infrastructure means the PoC output is reusable, not a throwaway prototype.
Built on top of your own Delta Tables
The PoC ingests your existing data, not a sandboxed demo. Validation reflects actual retrieval quality on your real knowledge base.
Improve security by keeping your data and model in-house
The entire PoC runs inside your Databricks managed environment. No proprietary data passes through an external API.
Reduce time to market
If the use case validates, a production build starts from a real foundation, not from zero.
High-level RAG LLM architecture on Databricks
Four phases. A working LLM chatbot on your data.
Assess knowledge base, define the Q&A use case, confirm model approach and architecture. Model selection locked in this phase.
End-to-end build: data ingestion, vector database setup, embedding pipeline, RAG architecture, Q&A chatbot layer. Tested against your data.
Chatbot deployed via Databricks Model Serving, a managed endpoint accessible to your team for real-world validation queries.
Monitoring set up to track response relevance, usage patterns, and degradation signals, objective basis for evaluating the PoC.
One package.
Everything included.
LLM PoC on Databricks
Looking for a production-ready, tiered engagement? See Applied LLM on Databricks
