it_support · saas · workflow
Benchling builds an LLM-powered RAG Slackbot for internal Terraform Cloud Q&A
Benchling engineers regularly asked Terraform Cloud questions in Slack, but finding answers required reading through a lengthy Confluence FAQ or spelunking through old Slack threads, making information retrieval slow and tedious.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Engineer asks in Slack
An engineer sends a Slack message explicitly tagging the @help-terraform-cloud user.
Tools used
Amazon BedrockOpenSearch ServerlessClaude 3.5 Sonnet v2Amazon Titan Text Embeddings v2AWS LambdaAWS API GatewaySlack · partnerConfluence · partner
Outcome
Benchling built a working RAG-based Slackbot that lets engineers get synthesized answers to Terraform Cloud questions from multiple internal and public data sources, and found the knowledge base setup took minutes instead of days.
Results
Time savedminutes instead of days
Grounding & classification
Source type: technical build writeup
21 fields verified against source quotes.
chatbotknowledge searchragknowledge basenamed customertools describedworkflow describedsoftwaretime savedtechnical build writeupit supportrag answering