What is the core goal of your project or what problem are you trying to solve with generative AI or

[Suheb]

What problem are you trying to solve using Databricks and generative AI?

[bianca_unifeye]

This is quite a generic question. The answer really depends on the industry, the type and quality of data available, and the budget or constraints for the solution.

Generative AI on Databricks can solve a wide range of problems, from text summarisation to code generation to intelligent searc, but the use case always varies by context.

Are you looking for something more specific?
If yes, could you share a bit more detail about:

• your industry or domain

• the business problem you’re trying to address

• the data sources you have

That will help in giving a more accurate and helpful answer.

[iyashk-DB]

Below are high-impact problems you can solve with Databricks and generative AI, grounded in your enterprise data and governed end to end.

Customer and employee knowledge access
Build RAG-powered knowledge assistants that answer questions over your proprietary documents, wikis, PDFs, and data—accurate, safe, and continuously evaluated.

Deploy customer support agents that find answers, summarize cases, and execute tasks, with integrated evaluation and guardrails to meet production quality bars.

Provide natural-language data access for analysts and business users via LakehouseIQ, which learns your org’s jargon and usage patterns for better answers.

Personalization and decisioning
Power structured RAG and real-time personalization by injecting user/account features (orders, status, risk) into prompts using Feature & Function Serving and online tables.

Automate recommendations, pricing, routing, and next-best-actions by joining LLMs with governed, low-latency context from your lakehouse.

Search and retrieval quality
Deliver high-recall, governed enterprise search with Vector Search: serverless indexing/sync from Delta tables, hybrid keyword+semantic retrieval, and UC-integrated ACLs.

Improve retrieval with embedding model finetuning and reranking to boost downstream RAG accuracy on in-domain data.

Document and content automation
Build document parsing/extraction pipelines (PDF/HTML) to convert unstructured content into structured fields for dashboards, QA, and downstream agents.

Enable multimodal RAG to search and reason across text, images, and complex PDFs using multimodal embeddings and Vector Search.

Governed deployment, evaluation, and monitoring
Enforce unified governance (access, lineage, auditing, sharing) across data, models, tools, and agents with Unity Catalog—no bolt‑on silos.

Measure and improve quality with MLflow Evaluation and Agent Evaluation (LLM-as-a-judge, custom scorers), consistent offline→online assessment, and trace-level root cause analysis.

Monitor production with AI Gateway-enabled inference tables to log requests/responses and join with usage and model details for observability and retraining loops.

Build and optimize AI agents, fast
Use the Mosaic AI Agent Framework to iterate on RAG and tool-calling agents quickly—including evaluation loops, guardrails, and one-click serving from Unity Catalog.

Prototype in AI Playground, author in code with MLflow ResponsesAgent, and deploy agents** to scalable Model Serving endpoints with credentials scoped by governance.

Model strategy and customization
Start with high-quality open models like DBRX (fast MoE, strong on code and reasoning), or query external/managed models through Foundation Model APIs under unified governance.

Fine-tune and adapt models to your domain with Mosaic AI Training and register/serve them in Unity Catalog to retain full control over weights and IP.

[iyashk-DB]

Below are high-impact problems you can solve with Databricks and generative AI, grounded in your enterprise data and governed end to end.

Customer and employee knowledge access
Build RAG-powered knowledge assistants that answer questions over your proprietary documents, wikis, PDFs, and data—accurate, safe, and continuously evaluated.

Deploy customer support agents that find answers, summarize cases, and execute tasks, with integrated evaluation and guardrails to meet production quality bars.

Provide natural-language data access for analysts and business users via LakehouseIQ, which learns your org’s jargon and usage patterns for better answers.

Personalization and decisioning
Power structured RAG and real-time personalization by injecting user/account features (orders, status, risk) into prompts using Feature & Function Serving and online tables.

Automate recommendations, pricing, routing, and next-best-actions by joining LLMs with governed, low-latency context from your lakehouse.

Search and retrieval quality
Deliver high-recall, governed enterprise search with Vector Search: serverless indexing/sync from Delta tables, hybrid keyword+semantic retrieval, and UC-integrated ACLs.

Improve retrieval with embedding model finetuning and reranking to boost downstream RAG accuracy on in-domain data.

Document and content automation
Build document parsing/extraction pipelines (PDF/HTML) to convert unstructured content into structured fields for dashboards, QA, and downstream agents.

Enable multimodal RAG to search and reason across text, images, and complex PDFs using multimodal embeddings and Vector Search.

Governed deployment, evaluation, and monitoring
Enforce unified governance (access, lineage, auditing, sharing) across data, models, tools, and agents with Unity Catalog—no bolt‑on silos.

Measure and improve quality with MLflow Evaluation and Agent Evaluation (LLM-as-a-judge, custom scorers), consistent offline→online assessment, and trace-level root cause analysis.

Monitor production with AI Gateway-enabled inference tables to log requests/responses and join with usage and model details for observability and retraining loops.

Build and optimize AI agents, fast
Use the Mosaic AI Agent Framework to iterate on RAG and tool-calling agents quickly—including evaluation loops, guardrails, and one-click serving from Unity Catalog.

Prototype in AI Playground, author in code with MLflow ResponsesAgent, and deploy agents** to scalable Model Serving endpoints with credentials scoped by governance.

Model strategy and customization
Start with high-quality open models like DBRX (fast MoE, strong on code and reasoning), or query external/managed models through Foundation Model APIs under unified governance.

Fine-tune and adapt models to your domain with Mosaic AI Training and register/serve them in Unity Catalog to retain full control over weights and IP.