If you’ve come here wondering how Databricks can help you harness Generative AI at scale, then you’re in the right place.
In this article, we take an in-depth look at the platform’s ability to link proprietary data with AI so that your solutions are custom and accurate.
Let’s get into it.
What is Generative AI on Databricks?
Databricks is a cloud-based data intelligence platform and machine learning platform that enables organizations to process large datasets and build AI-driven applications.
It’s the ideal foundation for generative AI solutions because it brings together data engineering, data science, and AI model deployment under one roof.
The platform is able to connect large datasets with powerful AI models, so businesses can train AI on real, high-quality data rather than generic internet sources everyone else also has access to.
The Role of Large Language Models (LLMs) in Databricks Generative AI
Large language models (LLMs) are the brains behind tools like AI-powered chatbots, document summarization, and smart automation.
They rely on natural language processing (NLP) to analyze and generate human-like responses.
But LLMs on their own are generic. To be truly useful they need fine-tuning on real business data.
Databricks helps companies train, refine, and deploy foundation model LLMs at scale, ensuring models are grounded in accurate, up-to-date enterprise data.
Once ready, these models run fast and cost-effectively with Databricks’ scalable GPUs and serverless inference. That means businesses can power real-time AI applications—like an AI assistant that pulls the latest customer data or a legal AI that drafts contracts using internal templates.
Instead of just adopting AI, companies can build AI that actually works for them.
How Databricks Enables Generative AI Models
Databricks integrates generative AI with its machine learning platform, so teams can go from experimentation to deployment without friction.
With MLflow, businesses can track AI experiments, fine-tune models, and test different hyperparameters. And when it’s time to scale, Databricks even supports distributed training on GPUs, making it possible to train large models faster.
Serverless model serving means you can deploy it instantly, whether it’s powering a chatbot, generating reports, or automating customer responses.
Databricks has expanded its AI ecosystem through its Mosaic platform—an acquisition that now underpins a suite of AI and ML tools, including Mosaic AI Model Serving, Mosaic AI Vector Search, Mosaic AI Playground, and Mosaic AI Foundational Models API.
Databricks also ensures AI stays smart and relevant. Features like vector search and Retrieval Augmented Generation (RAG) help models pull in real-time data.
Understanding Retrieval-Augmented Generation (RAG) with Databricks
Let’s get into the weeds a bit. While generative AI can’t access real-time proprietary data from your business, generative AI with RAG can.
It retrieves the latest proprietary data, making answers more accurate, context-aware, and trustworthy.
Here are some examples of how RAG can be used:
- Legal teams can build AI assistants that retrieve contract clauses from confidential internal databases, ensuring accurate and compliant document summaries.
- Customer support bots can access customer order histories and service records, delivering precise, personalized responses instead of generic replies.
- Finance teams can integrate AI with proprietary market insights, transaction data, and internal risk models, generating reports tailored to their business strategy.
- Healthcare providers can equip AI with private patient records and internal clinical guidelines, enabling more personalized and regulation-compliant recommendations.
- Retail businesses can train AI on exclusive inventory, pricing, and sales performance data, ensuring real-time, personalized shopping experiences.
- Research and development teams can leverage AI to analyze internal studies, intellectual property, and experimental data, speeding up innovation while maintaining data security.
Now, how does this work with Databricks? The platform connects structured and unstructured data directly to AI models.
It has vector search, which lets models retrieve documents, reports, or customer records in real time, ensuring AI-generated responses reflect up-to-date, enterprise-specific information.
Real-World Applications Generative AI with Databricks
Companies across industries are using Databricks to power real-world generative AI applications.
In need of inspiration? Here’s how three major organizations are leveraging Databricks’ AI capabilities:
Ordnance Survey: Smarter Geospatial Intelligence with AI
As Great Britain’s national mapping agency, Ordnance Survey turned to generative AI to enhance geospatial data analysis.
Using LLMs alongside the Segment Anything Model, they automated feature extraction from earth observation data, reducing manual work and improving location intelligence.
With Databricks, Ordnance Survey ensures its maps remain detailed, accurate, and AI-enhanced.
Northwestern Mutual: RAG-Powered Customer Service
In financial services, fast and accurate customer responses are critical. Northwestern Mutual implemented a RAG system using Databricks to power more context-aware, responsive AI assistants. Their solution indexes proprietary content, retrieves relevant data in real time, and continuously improves with user feedback loops.
The result? More reliable, AI-driven customer interactions that save time and reduce errors.
Block (formerly Square): AI-Powered Business Onboarding
Block streamlined its AI adoption by standardizing data infrastructure on Databricks, making generative AI more accessible across its platform.
With automated data import and AI-driven content generation, new businesses using Square can set up faster, optimize marketing materials, and personalize customer communications.
Databricks’ scalable AI framework helps Block reduce friction in onboarding and accelerate business growth.
Databricks Generative AI: Key Features and Benefits
Let’s look at Databricks’ key features and the benefits that come from having them.
A Unified Data & AI Platform
Databricks brings data, analytics, and AI together in one platform, eliminating the complexity of fragmented tools.
Its Lakehouse architecture ensures AI models are trained on high-quality, governed data, whether structured or unstructured.
More Efficient Model Training and Optimization
Training and fine-tuning LLMs is streamlined with MLflow, which tracks experiments, optimizes hyperparameters, and ensures reproducibility. Databricks also supports distributed training on GPUs, handling massive datasets efficiently.
Once models are ready, serverless model serving allows businesses to deploy AI instantly across apps, chatbots, and automation workflows.
Performance that Scales to Enterprise Needs
Scaling AI is effortless with elastic compute and efficient model inference. Whether training a single AI assistant or rolling out a global AI-powered workflow, Databricks adjusts compute power dynamically, ensuring seamless scaling without performance bottlenecks.
Collaborative AI Development
With Databricks Notebooks and Unity Catalog, data scientists, engineers, and business teams can collaborate in real time, reducing silos and accelerating deployment. Shared workspaces, experiment tracking, and version control make AI development faster and more efficient.
Building Generative AI Applications in Databricks: A Step-by-Step Overview
We’ve built generative AI applications for clients across industries, and one thing is clear: success depends on getting the data, model selection, and fine-tuning process right from the start.
As an official Databricks partner, we guide organizations through every stage—helping them move from raw data to AI-powered applications that deliver real business impact.
| Step 1 | Step 2 | Step 3 |
|---|---|---|
 Data Preparation and Ingestion |
 Selecting the Right Generative AI Mode |
 Fine-Tuning and Training Models |
Step 1: Data Preparation and Ingestion
Every successful AI project starts with high-quality data. We’ve helped clients implement Databricks Delta Lake to create real-time, scalable data pipelines that clean, structure, and store massive datasets.
Without a solid data foundation, AI models produce unreliable results—so we make sure data ingestion is efficient, automated, and built for long-term success.
Step 2: Selecting the Right Generative AI Model
Choosing the right LLM depends on the business use case, performance requirements, and governance needs.
When it comes to this step, we’ve always guided clients through selecting and fine-tuning open-source models like Llama and Falcon, as well as proprietary GPT-based architectures when security and customization are key.
With MLflow, we track every experiment, compare performance, and ensure each iteration delivers meaningful improvements.
At the end, you get to deploy a model optimized for your industry, whether it’s finance, healthcare, retail, or something else entirely.
Step 3: Fine-Tuning and Training Models
Fine-tuning is where AI moves from generic to business-ready.
We’ve helped clients use Databricks’ distributed training to refine models with proprietary datasets, making AI outputs more relevant and accurate. Instead of relying on generic internet data, we train models on real business inputs, whether for contract analysis, fraud detection, or customer interactions.
With Databricks’ ML pipelines, we help organizations build AI models that are not only scalable and efficient but also ready for real-world deployment from day one.
Enhancing Databricks AI Solutions with Microsoft Azure
Microsoft Azure is a cloud computing platform that provides scalable infrastructure, AI services, and enterprise-grade security for businesses looking to build and deploy AI applications.
If you run Databricks on Azure, you can take advantage of cloud integration, high-performance compute, and a secure environment to accelerate generative AI development.
Here’s what’s possible when using the two together:
- Scale AI workloads dynamically with Azure’s elastic compute, ensuring smooth performance for large-scale model training and inference.
- Enhance security and compliance with enterprise-grade protection, making it ideal for organizations handling sensitive or regulated data.
- Seamlessly integrate with Azure AI Services, enabling Databricks models to connect with tools for intelligent search, document processing, and AI-driven automation.
- Leverage high-performance cloud infrastructure to speed up AI development and deployment without managing complex hardware.
- Unify data, analytics, and AI workflows by combining Databricks’ Lakehouse architecture with Azure’s cloud ecosystem for streamlined operations.
- Deploy and scale AI applications faster using Azure’s managed services, reducing infrastructure overhead while maintaining reliability.
Want to know more about Azure and how it helps you access higher-performing AI? Check out Microsoft’s AI Services documentation.
Best Practices for Implementing Generative AI with Databricks
With the right actions, businesses can develop, deploy, and scale generative AI with Databricks in a way that delivers real, measurable value.
Here’s how we help clients implement generative AI with Databricks effectively.
1. Choose the Right Use Case
Not every problem needs generative AI. Focus on high-value applications where AI can drive real impact—such as automating customer service, generating personalized content, or enhancing data-driven decision-making.
The best use cases leverage AI to augment human workflows, not replace them. Though, saying that, there are developments that suggest AI can work more as a co-worker rather than a digital assistant.
If you want to hear more about that, check out our podcast episode with Patrick Lynch, PhD, AI Faculty Lead at Hult International Business School.
2. Optimize Data Pipelines
AI models are only as good as the data they learn from. Databricks Delta Lake ensures data is clean, reliable, and available in real time by handling ingestion, transformation, and storage in a unified environment. A well-structured pipeline eliminates data silos and inconsistencies, making models more accurate and effective.
3. Deploy RAG Solutions
We covered the benefits of RAG already but it’s a key part of getting your proprietary data into a Generative AI solution.
Want to dig deeper into RAG? We have plenty of resources to help:
- The CTO’s Blueprint to Retrieval Augmented Generation (RAG)
- Testing Your RAG-Powered AI Chatbot
- RAG for Technology: Use-Cases, Impact, & Solutions
- RAG for Communications: Use-Cases, Impact, & Solutions
- RAG for Retail: Use-Cases, Impact, & Solutions
- RAG in Financial Services: Use-Cases, Impact, & Solutions
- Harnessing RAG in Healthcare: Use-Cases, Impact, & Solutions
4. Monitor Model Performance
AI models require continuous monitoring and refinement. With MLflow, teams can track model behavior, fine-tune hyperparameters, and compare different iterations to improve performance over time.
Setting up automated monitoring helps detect drift, bias, or performance issues before they impact real-world applications.
Challenges of Using Generative AI Models and How Databricks Solves Them
Businesses struggle with messy data, unpredictable model behavior, and the sheer computing power needed to scale AI applications.
We’ve seen these challenges firsthand while helping clients deploy AI solutions. We’ve also seen how the Databricks platform addresses these challenges.
Data Complexity: AI Can’t Learn from What It Can’t Trust
Most organizations deal with fragmented, unstructured, and constantly changing data.
Traditional systems aren’t built for AI’s appetite for real-time, high-quality inputs. With Databricks’ Lakehouse architecture, teams can consolidate structured, semi-structured, and unstructured data into a single source of truth.
Delta Lake makes AI-ready data accessible by automating ingestion, cleaning, and version control, so models train on the right information every time.
Model Bias & Drift: AI That Improves Instead of Degrades
Left unchecked, AI models can reinforce biases or degrade in accuracy as data evolves.
We help clients use Databricks’ built-in MLflow tools to monitor LLM performance, track drift, and retrain models as needed. With automated logging, bias detection, and validation, businesses can determine when an AI model needs fine-tuning because it’s tracked in a single, transparent workflow.
Scalability: AI Without the Infrastructure Headaches
Databricks’ distributed computing and GPU-accelerated training allow businesses to fine-tune LLMs without hitting performance bottlenecks.
We’ve seen clients cut training times from weeks to days while keeping infrastructure costs under control. Whether deploying AI-powered search, automation, or predictive analytics, Databricks provides the flexibility to scale without rewriting pipelines or rethinking infrastructure.
Frequently Asked Questions (FAQs) About Databricks Generative AI
How does Databricks integrate LLMs?
Databricks provides a seamless environment for integrating large language models (LLMs) by combining its Lakehouse architecture with machine learning workflows.
With MLflow, teams can track, fine-tune, and deploy models efficiently, while GPU-accelerated training ensures high performance. It supports open-source models like Llama and Falcon, as well as proprietary architectures, making AI development scalable and efficient.
What is RAG, and how does it work in Databricks?
Retrieval-Augmented Generation enhances AI models by allowing them to retrieve real-time proprietary data before generating responses. In Databricks, RAG is powered by vector search and Delta Lake, ensuring AI-generated content is context-aware, accurate, and up to date.
This approach is particularly useful for industries requiring domain-specific insights, such as finance, healthcare, and legal services.
How secure is Generative AI implementation on Databricks?
Databricks prioritizes security with enterprise-grade governance, access controls, and encryption protocols. The platform integrates seamlessly with cloud security frameworks, ensuring compliance with industry standards such as GDPR and HIPAA.
Unity Catalog further enhances security by providing fine-grained permissions for AI models and training datasets, reducing risk and ensuring responsible AI deployment.
Unlock the Full Potential of Databricks Generative AI with HatchWorks AI
If you’re ready to take Generative AI to the next level with Databricks, you don’t have to navigate the complexities alone.
At HatchWorks AI, we specialize in helping businesses implement, fine-tune, and scale AI solutions that drive real results.
Whether you need support with LLM integration, RAG implementation, or optimizing your AI workflows, our team of Databricks experts is here to guide you.
Let’s turn your data into intelligence and your AI investments into impact. Get in touch with HatchWorks today and see how we can help you build AI solutions tailored to your business needs.
👉 Learn more and get started: HatchWorks AI Databricks Services
Other AI Resources You’ll Love
All about prompt engineering – Expert’s Guide: Generative AI Prompts for Maximum Efficiency
All about fine-tuning LLMs – How to Train and Fine Tune a Multimodal Language Model [+ Use Cases]
All about the hacks we use to build MVPs with Gen AI – Gen AI Hacks That Will Cut Development Time in Half: How to Guide
