HP's journey into production LLM deployment represents a significant case study in practical GenAI implementation within a large enterprise context. The company, which supports over 200 million printers worldwide, faced substantial challenges in their data operations that led them to explore LLM-based solutions. This case study provides valuable insights into the real-world implementation of LLMOps in a corporate setting. The initial problem space was well-defined: HP's data engineering teams were becoming overwhelmed with support requests, spending up to 30% of their time handling basic queries about data models, platform features, and access requests. This situation was creating a significant bottleneck in their operations and driving up costs. The challenge was compounded by their information being scattered across various internal systems including wikis, SharePoint files, and support channels. From an architectural perspective, HP's implementation demonstrates several key LLMOps best practices: * **Infrastructure Foundation**: They built their solution on the Databricks Data Intelligence Platform on AWS, utilizing a lakehouse architecture to unify their data, analytics, and AI operations. This provided a solid foundation for their GenAI implementations with built-in governance and security features. * **Model Selection Process**: The team used Mosaic AI Playground to experiment with different LLMs before selecting DBRX as their production model. This selection was based on both performance and cost-effectiveness considerations, highlighting the importance of thorough model evaluation in LLMOps. * **RAG Implementation**: Their solution incorporated Retrieval Augmented Generation (RAG) with a Vector Search database backend. This approach helped ensure that the chatbot's responses were grounded in HP's actual documentation and policies, reducing hallucination risks and improving accuracy. The technical architecture of their chatbot system included several key components: * A web frontend for user interactions * A backend agent system that: * Parses user input * Searches the vector database * Retrieves relevant information * Interfaces with the GenAI endpoint * A web crawler system that: * Automatically crawls internal information sources * Tokenizes the content * Populates the Vector Search database * URL reference system for answer validation From a governance and security perspective, HP implemented several important controls: * Unity Catalog for data management and governance * Fine-grained access control for their 600+ Databricks users * Data lineage tracking * Comprehensive auditing capabilities * DSR (Data Subject Request) compliance features What's particularly noteworthy about this implementation is the speed and efficiency of deployment. The entire end-to-end solution was built by an intern in less than three weeks, which is remarkable compared to similar projects that often take months with experienced engineers. This speaks to the maturity of the tools and platforms used, as well as the effectiveness of their architectural decisions. The results of this implementation were significant: * Reduced operational costs by 20-30% compared to their previous AWS Redshift warehouse * Significant reduction in manual support requests to the data engineering team * Improved self-service capabilities for partners and business leadership * Enhanced knowledge accessibility across the organization HP's approach to scaling and expanding their GenAI implementation is also instructive. They're not treating this as a one-off project but rather as a foundation for broader AI adoption: * They're exploring AI/BI Genie to add efficiency to service teams * Building pre-canned query workspaces for frequently asked questions * Planning to expand GenAI solutions to customer-facing applications * Focusing on troubleshooting and query resolution processes From an LLMOps perspective, several key lessons emerge from this case study: * The importance of starting with a well-defined, high-impact use case * The value of using established platforms with built-in governance and security features * The benefits of implementing RAG to ground LLM responses in actual company data * The effectiveness of an iterative approach to deployment and scaling * The importance of measuring and monitoring cost impacts The case study also highlights some potential limitations and areas for careful consideration: * The heavy reliance on a single vendor's ecosystem (Databricks) * The need for ongoing maintenance of the web crawler and vector database * The importance of keeping training data up to date * The need for continuous monitoring of response quality and accuracy Overall, HP's implementation provides a valuable template for enterprises looking to deploy LLMs in production, particularly for internal knowledge management and support functions. Their success demonstrates that with the right architecture and tools, significant value can be derived from LLM implementations relatively quickly and cost-effectively.