LLM
Recent items mentioning LLM across the Databricks ecosystem — releases, news, videos, and community Q&A. Updated hourly.
Databricks solutions are pairing LLMs with AI gateways and Genie spaces to automate specialized industry workflows, including higher education advisory call scoring 3 and rapid energy theft detection 1. Hands-on implementation guidance now covers building common-sense product recommendation engines 4 as well as constructing custom seven-step Python RAG pipelines using Groq models 2.
Generated daily from the 4 most recent items mentioning LLM. Click any [N] to jump to the source.
TutorialsDetect Energy Theft Faster with Genie
Databricks demonstrates an end-to-end AI application that detects energy theft, automates investigations, and generates executive reports using Unity Catalog and Genie. The video walks through an architecture featuring Lakebase for transactional storage, model serving for machine learning and LLMs, and AI gateways for governance and cost control.
NewsRAG Explained + Build a RAG App From Scratch in Python using LLM | Chapter 08
This video teaches the core concepts of retrieval-augmented generation and demonstrates how to build a complete RAG application from scratch in Python using a Groq language model. The tutorial covers a seven-step pipeline including document ingestion, token-based text chunking, vector embedding generation, in-memory storage, similarity search, prompt augmentation, and response generation.
AI-Enabled Advisory Services for Higher Education
Databricks has introduced a GenAI-powered solution that automates and scales the quality review of higher education advisory calls by transcribing conversations, scoring performance against institutional rubrics, and surfacing insights. The blog post demonstrates this architecture on a single, governed platform, providing code notebooks and a Genie space for natural language data exploration.
Solution Accelerator Series | Building Common Sense Product Recommendations With LLMs
NewsAI-Ready Data on Databricks: How TK Elevator Uses Context and Meaning to Make AI Agents Work
TK Elevator uses Databricks' Unity Catalog to create "AI-ready data" by harmonizing data from over 100 disparate systems, enabling a common language for their AI agents. This foundation supports predictive maintenance for elevators, empowering 25,000 service technicians with tailored support and voice debriefing capabilities.
Guide to Agentic Systems and AI Agents
Agentic AI systems are autonomous software platforms that perceive, reason, execute multi-step tasks, and learn with minimal human intervention, unlike traditional generative models. These systems use LLMs as reasoning engines with external tools and memory to complete complex workflows, with enterprise adoption spanning customer service to financial risk.
End-to-End RAG Workflow: How Retrieval Augmented Generation Works
Databricks now offers a five-stage RAG workflow for connecting LLMs to external knowledge bases, enabling accurate, domain-specific answers without model retraining. Production RAG requires careful selection of embedding models, vector database indexing, chunking strategies, and hybrid search, with independent evaluation of retrieval precision and generation faithfulness.
NewsAn agentic CDP empowers marketing teams to move from static campaigns to Infinity Campaigns
Databricks introduces "Infinity Campaigns," a new marketing approach where an AI agent processes customer signals to determine the next best action and generate personalized content. This creates a continuous feedback loop where customer interactions generate new signals, feeding back into the agent for ongoing optimization.
EventsIntroducing CustomerLake: The Agentic CDP | Ali Ghodsi Databricks CEO at Data + AI Summit
Databricks introduces CustomerLake, an agentic Customer Data Platform built on the lakehouse architecture. It features a profile agent for identity deduplication using LLMs and a campaign agent for personalized, one-to-one "infinity campaigns."
What is document AI?
Document AI transforms messy, high-volume documents into structured data for downstream systems, offering value beyond just faster processing. While generative AI makes it more adaptable for summarization and extraction, accuracy still relies on validation and human review, with governance becoming central due to sensitive data.
How Ecolab rebuilt retail intelligence on Databricks and Anthropic Claude
Ecolab rebuilt retail intelligence on Databricks and Anthropic Claude, converting 700-page FDA manuals into real-time answers for frontline staff using Foundation Model APIs and cutting compliance report compilation from two weeks to under two minutes. The solution, a native Databricks App with Lakebase Postgres and Unity Catalog, unifies nine siloed data sources and employs a multi-agent orchestration framework with Judge LLMs and MLflow tracing for personalized, continuously refined intelligence.
AI Serving Platform That Adapts to Your Model
Databricks now offers a fully managed AI serving platform that automatically adapts to your model's resource needs, from scikit-learn to 70B LLMs, without manual configuration. This results in up to 90% lower infrastructure costs and <10ms p99 latency overhead for customers migrating from self-managed stacks.
Announcing the Databricks storage ecosystem: Governing the enterprise data estate, wherever it lives
The Databricks Storage Ecosystem now natively connects hybrid and on-premises storage platforms to Databricks via OpenSharing, enabling centralized data governance and GenAI scaling across your entire hybrid infrastructure. Run Databricks Serverless Compute, Genie, and LLMs directly on your on-premises datasets with a zero-copy architecture, instantly turning isolated data into active, AI-ready assets.
NewsHow LLMs Understand your Prompts: Tokenization & Embeddings | Chapter 05
The video explains how Large Language Models (LLMs) understand text by converting it into numerical representations through tokenization and embeddings. It demonstrates how text is broken into tokens, assigned unique IDs, and then transformed into dense vectors (embeddings) that capture semantic meaning and positional information for LLM processing.
NewsWhen to choose CPU vs GPU: Databricks AI Runtime Explained
CPUs are best for data work like ETL, feature engineering, SQL, and classical machine learning, while GPUs are designed for deep learning workloads such as fine-tuning LLMs and training neural networks. Databricks AI Runtime simplifies GPU usage by providing serverless Nvidia GPUs, removing the need for manual infrastructure setup and allowing seamless transitions between CPU for data prep and GPU for model training within the Databricks environment.
TutorialsHow Large Language Models (LLMs) Work - Full Explanation | Chapter 04
Large Language Models (LLMs) are text-based neural networks trained on massive data to predict the next word (token), operating through tokenization, vector embeddings, and a transformer architecture. LLMs undergo pre-training, supervised fine-tuning, and reinforcement learning from human feedback to become helpful, safe, and aligned, with concepts like context length, knowledge cut-off, and hallucination defining their capabilities and limitations.
Accelerating LLM Inference with Prompt Caching for Open‑Source Models on Databricks
Databricks now supports prompt caching for open-source models across all workloads, automatically accelerating LLM inference by reusing repeated prompt prefixes. This feature boosts throughput by 2.5x and reduces P50 latency by 3x for models like GPT-OSS, with no setup required.
TutorialsBuilding Trustworthy, High-Quality AI Agents with MLflow
Databricks' MLflow platform helps developers build trustworthy, high-quality AI agents by providing tools for end-to-end observability, evaluation, prompt management, and AI gateway governance. It demonstrates how MLflow facilitates tracing, expert feedback collection, automated issue detection with LLM judges, prompt optimization, and continuous monitoring throughout the agent development lifecycle.
TutorialsAI Agents That Remember: Building Stateful Systems with Lakebase
AI agents require four types of memory (working, episodic, entity, procedural) to be truly intelligent and stateful, which traditional databases struggle to provide. Databricks Lakebase, built on Postgres, offers a unified OLTP and OLAP solution with features like serverless auto-scaling and Git-style branching to manage these complex memory needs for AI agents.
NewsGovern MCP servers in Databricks #databricks #mcp #aigovernance
Databricks Unity AI Gateway now governs MCP servers, centralizing their management alongside built-in foundation models and LLMs. This integration allows for easier governance and orchestration of various AI components and agents within Databricks.
TutorialsHow to Build an AI Security Governance Hub with Agent Bricks
Databricks Agent Bricks enables building an AI Security Governance Hub by transforming static security playbooks into adaptive multi-agent systems. The video demonstrates combining a knowledge assistant for unstructured documents and a Genie space for structured data into a supervisor agent, then details how to tune and monitor these agents for improved performance and data privacy.
EventsBuilding Trustworthy, High-Quality AI Agents with MLflow
MLflow provides a comprehensive platform for building, evaluating, and deploying high-quality AI agents, offering tools for observability, automated evaluation, prompt optimization, and production monitoring. It enables developers to streamline the agent development lifecycle, from prototyping and testing with human and AI judges to fixing issues and ensuring reliable, governed deployment.
LLM Vs AI: A Practical Guide to Differences, Use Cases, and Tools
LLMs are a subset of AI, and this guide clarifies their practical differences, use cases, and tools. Understand how LLMs fit into the broader AI landscape and what that means for your Databricks workflows.
AI Applications: Tools, Use Cases, and Platforms
AI applications span four capability tiers, each with distinct data requirements and evaluation frameworks, and enterprise deployments often stall due to inadequate data infrastructure. Production-grade model development, from prompt engineering to pretraining, is increasingly accessible with open-source LLMs, but requires pre-built governance and monitoring infrastructure for successful deployment at scale.
NewsFrom AI to Agents| Fundamentals of AI | ML | DL | LLM & GenAI | Chapter 01
The video explains the fundamental concepts of AI, ML, DL, LLMs, and GenAI, illustrating their hierarchical relationship as subsets of each other. It also defines what models are (mathematical formulas trained on data) and how agents combine LLMs with tools and optional memory to perform autonomous tasks.
Are LLM agents good at join order optimization?
LLM agents can improve Databricks join order optimization, achieving 1.3x latency reduction in 80% of cases by reasoning through runtime statistics. This prototype demonstrates LLM agents' potential to act as data-driven DBAs, addressing cardinality misestimation challenges in complex SQL queries.
NewsGenAI - For Data Engineers Agenda & Introduction | LLM & Agentic AI | LangChain & LangGraph | Claude
This video introduces a new course, "GenAI for Data Engineers," designed to teach data engineers how to leverage generative AI, LLMs, and agentic AI. The course covers basics of LLMs, building agents with LangChain and LangGraph, using Cloud Code, and applying agentic AI within Databricks and data engineering workflows.
Introducing MLflow AI Gateway: Governed, Observable Access to LLMs
MLflow AI Gateway provides a single, secure endpoint for all LLM providers, complete with usage tracking and native tracing. This new feature offers governed, observable access to LLMs for Databricks practitioners.
MemAlign: Building Better LLM Judges From Human Feedback With Scalable Memory
MemAlign, a new framework for aligning LLMs with human feedback, is now available, offering competitive or better quality than state-of-the-art prompt optimizers at significantly lower cost and latency. It achieves this through a lightweight dual-memory system, making it a valuable tool for building better LLM judges.
NewsVibe-Engineering LakeFlow Pipelines, the Advancing Analytics Way
Advancing Analytics introduces Lake Forge, an engineering framework that uses LLMs and an agentic workflow to generate standardized LakeFlow pipeline templates from data specifications. This system aims to enable scalable, repeatable, and supportable data pipeline creation by balancing AI-driven "vibe coding" with human-engineered guardrails and validation loops.
NewsAI Agents in Action: Structuring Unstructured Data on Demand With Databricks and Unstructured
TutorialsSponsored by: West Monroe | Disruptive Forces: LLMs and the New Age of Data Engineering
Unity Catalog AI 0.2.0
Unity Catalog AI 0.2.0 adds Gemini and LiteLLM integrations for using UC functions as AI tools and introduces function wrapping APIs with dependency management support. AutoGen integration now requires version 0.4.x and Databricks client now only supports serverless endpoints.
NewsSponsored: EY | Business Value Unleashed: Real-World Accelerating AI & Data-Centric Transformation
NewsAI Regulation is Coming: The EU AI Act and How Databricks Can Help with Compliance
NewsIFC's MALENA Provides Analytics for ESG Reviews in Emerging Markets Using NLP and LLMs
NewsLive from the Lakehouse: Machine Learning, LLM & market changes over the past decade & data strategy
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