customer_support · logistics · workflow
DoorDash improves Dasher support with LLM-based RAG chatbot, reducing hallucinations by 90%
DoorDash's existing automated support relied on rigid flow-based resolution paths that could address only a small subset of Dasher issues. The knowledge base was also hard to navigate, time-consuming to read, and available only in English despite many Dashers preferring other languages.
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 · Dasher submits issue
The workflow begins when a Dasher presents an issue to the chatbot.
Tools used
RAGGPT-4Claude-3LLM GuardrailLLM JudgePromptfoovector store
Outcome
The LLM Guardrail reduced hallucinations by 90% and compliance issues by 99%, and the chatbot now autonomously assists thousands of Dashers each day while freeing human agents to focus on more complex problems.
What failed first
The initial LLM RAG chatbot produced responses that diverged from the intended knowledge base context, with LLMs drawing on potentially erroneous public sources such as Quora, Reddit, and Twitter rather than grounded DoorDash information.
Results
Volume90%
Grounding & classification
Source type: technical build writeup
36 fields verified against source quotes.
conversational aiknowledge searchragsummarizationsupport agentchat transcriptknowledge basefailure mode describedhuman review describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedecommercelogisticsaccuracy improvementautomation rateemployee productivityerror reductiontechnical build writeupcustomer supportticket triageautonomous resolutionescalation workflowrag answering