Claude Code

Al Chen is a field engineer at Galileo, an observability platform for AI applications, where he works on the front lines with enterprise customers asking highly technical questions. Despite never having held an engineering role, Al has built a system using Claude Code to query Galileo’s 15 separate repositories, combine that with Confluence documentation and customer-specific quirks, and deliver hyper-personalized answers that would otherwise require constant engineering support. *What you’ll learn:* 1. How to use Claude Code to query multiple repositories simultaneously for customer support 2. Why code is often a better source of truth than documentation 3. How to combine repository context with Confluence and Slack using MCPs 4. The “customer quirks” system that creates hyper-personalized deployment guides 5. How to build virtuous loops that turn single customer questions into scalable knowledge 6. Why information organization matters less in the AI era 7. A simple 16-line script (written by Claude Code) that pulls the latest main branch across all your repositories to keep your context current 8. How to reduce engineering interruptions to near-zero by empowering customer-facing teams to query the codebase directly *Brought to you by:* Orkes—The enterprise platform for reliable applications and agentic workflows: https://www.orkes.io/ Tines—Start building intelligent workflows today: https://tines.com/howiai *In this episode, we cover:* (00:00) Introduction to Al Chen (02:50) The problem: documentation wasn’t enough (04:23) Pulling 15 repos into VS Code (06:03) How Claude Code queries the entire codebase (08:00) Why current code beats documentation (08:31) The pull script that keeps everything updated (09:54) Opening projects at the multi-repo level (11:40) Live demo: answering deployment questions (13:25) The customer quirks system (15:00) Living in chaos: why organization matters less now (17:03) Competing on customer experience, not just product (18:20) Should customers be able to query the code directly? (20:05) Where humans still add value (25:46) Using AI for reactive Slack support (29:16) The “and then” workflow discovery (32:07) Scaling processes across the team (34:07) Lightning round and final thoughts *Detailed workflow walkthroughs from this episode:* • How Al Chen Uses Claude Code and 15 Repos to Answer Any Customer Question: https://www.chatprd.ai/how-i-ai/claude-code-and-repos-to-answer-any-customer-question • Automatically Create a Knowledge Base from Slack Support Threads: https://www.chatprd.ai/how-i-ai/workflows/automatically-create-a-knowledge-base-from-slack-support-threads • How to Use AI to Answer Customer Questions from Your Entire Codebase: https://www.chatprd.ai/how-i-ai/workflows/how-to-use-ai-to-answer-customer-questions-from-your-entire-codebase *Tools referenced:* • Claude Code: https://claude.ai/code • VS Code: https://code.visualstudio.com/ • Pylon: https://usepylon.com/ • Confluence: https://www.atlassian.com/software/confluence *Other references:* • Slack: https://slack.com/ • Kubernetes: https://kubernetes.io/ • Stack Overflow: https://stackoverflow.com/ • Intercom: https://www.intercom.com/ *Where to find Al Chen:* LinkedIn: https://www.linkedin.com/in/thealchen/ Company: https://www.rungalileo.io *Where to find Claire Vo:* ChatPRD: https://www.chatprd.ai/ Website: https://clairevo.com/ LinkedIn: https://www.linkedin.com/in/clairevo/ X: https://x.com/clairevo _Production and marketing by https://penname.co/._ _For inquiries about sponsoring the podcast, email jordan@penname.co._