Building a Knowledge Base Chatbot for Data Team Support Using RAG

hp · Tech · 2024 · source Agentic: yes · promotional bias: high · source-backed · extracted with prompt 0.5.4 (schema v2)

Generated from extraction.json — do not edit by hand. Every claim cites verbatim source evidence; stated means the quote directly asserts the claim, implied means it supports it indirectly.

Problem

Domain: Enterprise data-platform support

HP needed to reduce the support burden on data engineering teams who were repeatedly answering data-access, SQL, onboarding, and troubleshooting questions for thousands of internal data users.

Constraints:

• manual SQL queries and cross-referencing created latency for support teams
• restricted data access required governance, privacy, lineage, and auditing
• information was scattered across internal wiki pages, SharePoint files, and team support channels
• nontechnical users needed reliable answers without waiting on data scientists or engineers
• the implementation had to fit HP's existing cloud migration and lakehouse architecture

Architecture

HP built a Databricks-backed RAG chatbot for data-platform support. The system uses a web front end, a back-end agent that parses user input, AI Search as the retrieval database, a web crawler that tokenizes internal information from internal sites, and a GenAI endpoint that generates answers with source URLs. Unity Catalog provides fine-grained access control, lineage, and auditability for hundreds of Databricks users.

Diagram confidence: explicit (topology described by the source).

flowchart LR
  internal-knowledge-sources[Internal knowledge sources] --> web-crawler[Web crawler]
  web-crawler --> ai-search-database[AI Search database]
  web-front-end[Web front end] --> back-end-agent[Back-end agent]
  back-end-agent --> ai-search-database
  back-end-agent --> genai-endpoint[GenAI endpoint]
  databricks-sql-and-lakehouse[Databricks SQL and lakehouse] --> unity-catalog[Unity Catalog]
  unity-catalog --> ai-search-database
  databricks-sql-and-lakehouse --> back-end-agent

Components

Data flow

A crawler ingests and tokenizes internal wiki, SharePoint, and support-channel content into AI Search. A user submits a question through the web front end. The back-end agent parses the input, retrieves relevant content from AI Search, sends retrieved context to the GenAI endpoint, and returns an answer with URLs so the user can validate or explore the source material. Databricks SQL, Unity Catalog, and the lakehouse platform provide compute, governance, and access controls.

Why this architecture

HP chose a Databricks-native RAG architecture because its data, governance, SQL, and GenAI tooling could live on the same lakehouse platform. That avoided copying data into separate AI workspaces and let the team build quickly while preserving access control, lineage, and reference URLs.

Models used: DBRX · LLMs evaluated in Agent Bricks AI Playground · Databricks AI/BI Genie

Scale

• HP supports functions for more than 200 million printers worldwide.
  “HP, a global leader in computing and printing solutions, supports the functions of over 200 million printers worldwide. However, manual SQL queries and cross-referencing solutions slowed down service teams, often requiring assistance from data scientists for troubleshooting and real-time”article, stated
• The governed Databricks environment serves more than 600 users.
  “Serverless Databricks SQL allows us to provision ephemeral compute clusters on demand, and Unity Catalog is used for data management and governance, making it very easy to control fine-grained access for our 600+ Databricks users and enabling them to access information via a knowledge base chatbot.”article, stated
• Support work consumed 20% to 30% of data-team time, equivalent to one full-time employee for a five-person team.
  “our teams were spending 20–30% of their time trying to whip up SQL queries, dig into the data and cross-reference in different areas”article, stated

  “at HP, explained, “With all those inquiries coming in, our teams were spending 20–30% of their time trying to whip up SQL queries, dig into the data and cross-reference in different areas to figure out what was going on. With a five-person team, this equates to an additional full-time employee.”article, stated
• An intern implemented the end-to-end chatbot in less than three weeks.
  “internal information on various sites and gets them populated into the AI Search database. The college intern implemented the end-to-end solution in less than three weeks on Databricks, which is amazingly fast compared with other teams who spent months building similar solutions on other”article, stated
• The Databricks migration produced 20% to 30% lower operational costs than AWS Redshift.
  “HP has seen significant operational cost savings compared with their AWS Redshift warehouse, to the tune of 20–30% lower costs.”article, stated

Alternatives considered (roads not taken)

Manual data-team support

Rejected because: Manual SQL writing, cross-referencing, and access support consumed too much team capacity and delayed decisions.

Moving data into separate AI workspaces

Rejected because: Copying data raised governance, privacy, and deletion-request complexity, so HP preferred a platform where AI tools sat with governed data.

AWS Redshift as the main warehouse foundation

Rejected because: HP migrated to Databricks SQL for cost, performance, isolation, attribution, cataloging, access control, and governance.

Patterns

rag-retrieval (knowledge_retrieval)

HP built a RAG chatbot over internal wiki, SharePoint, and support-channel knowledge so users could self-serve data-platform answers.

tool-use-orchestration (agent_architecture)

The back-end agent parses input, searches, retrieves relevant data, and calls the GenAI endpoint as a small tool chain.

model-routing (model_operations)

HP evaluated multiple LLMs in Agent Bricks and selected DBRX as the cost-effective model for the chatbot.

privacy-preserving-processing (governance)

Unity Catalog kept GenAI access tied to governance, lineage, auditing, and fine-grained permissions instead of copying data into a separate AI environment.

Tradeoffs

Build on the Databricks platform

Chose Lakehouse-native AI, SQL, Unity Catalog, and AI Search over A separate AI stack beside the data warehouse.

Rationale: Keeping AI and data on one governed platform reduced reinvention and helped manage fine-grained access, lineage, and audit needs.

Use RAG with reference URLs for support answers

Chose Retrieved internal knowledge plus generated answers and URLs over Free-form chatbot answers without source validation.

Rationale: Support users could validate or explore answers further, which matters for internal data-platform troubleshooting.

Select DBRX for the chatbot

Chose DBRX over Other LLMs tested in the Agent Bricks AI Playground.

Rationale: The source says DBRX was the most appropriate and cost-effective model for HP's chatbot needs.

Gotchas (what bit them)

Manual support work becomes hidden staffing cost.

Root cause: Data users depend on engineering teams for SQL, troubleshooting, access, and cross-referencing when knowledge is scattered.

Resolution: Use a RAG chatbot with source URLs to deflect common support and onboarding questions.

AI workspaces can create governance duplication.

Root cause: Moving data out of the warehouse for AI can require copies that complicate privacy, security, and deletion requests.

Resolution: Build the chatbot on a data-centric platform with Unity Catalog governance and lakehouse-native AI tooling.

Internal knowledge sprawl blocks self-service.

Root cause: Onboarding and support information was spread across wiki pages, SharePoint, and support channels.

Resolution: Crawl and tokenize internal sites into AI Search and return reference URLs with generated answers.

Evaluation

The article does not describe a formal evaluation harness for chatbot answer quality; it reports model selection through Agent Bricks experimentation, source URLs for user validation, and operational outcomes such as reduced support burden and lower warehouse costs.

Metrics: 20% to 30% lower operational costs than AWS Redshift · 20% to 30% of data-team time previously spent on support · less than three weeks to implement the chatbot · 600+ Databricks users with fine-grained access

Outcomes

• HP built a functional end-to-end RAG assistant in less than three weeks.
• The Databricks migration lowered operational costs by 20% to 30% versus AWS Redshift.
• The chatbot is expected to reduce manual partner-support work for data practitioners.
• HP is exploring AI/BI Genie for natural-language data querying and dashboard creation.

The article anchors the deployment in HP's enterprise-scale data environment: over 200 million printers supported, 600+ Databricks users, and a data support burden that previously consumed 20% to 30% of a data team.