ModelRefs / RAG Support Bot — Canonical Workflow
RAG Support Bot — Canonical Workflow
RAG Support Bot: provisional AI workflow implementation reference with candidate models, providers, tools, and architecture.
Overview
A RAG support bot retrieves the most relevant help-centre articles for each inbound query using semantic search, passes the retrieved context to a language model for grounded answer generation, and cites every source by article title and section. When the model's confidence falls below a configurable threshold, the conversation is escalated to a human agent with a full context packet including the query, retrieved sources and the model's draft answer. The bot handles tier-1 queries automatically while ensuring human oversight on uncertain or high-stakes requests.
Implementation profile
| Category | llms |
|---|---|
| Implementation maturity | production |
| Primary use cases | customer-support, rag |
| Deployment options | managed-api, hybrid |
| Architectures | serverless-api, managed-container, edge-runtime |
Candidate models with published references
- BGE-M3
- GPT-5
- GPT-5 Mini
- Claude Sonnet 4.5
- Claude Opus 4
- Gemini 3 Pro
- Llama 4 Scout
- DeepSeek R1
- Mistral Large 2
- Command R+
- o3
- o4 Mini
Coverage means the model is a candidate worth evaluating for this workflow, not a ranking or a recommendation. Models whose reference pages are still in review are omitted.
Benchmarks relevant to this workflow
- Chatbot Arena ELO — LMSYS crowd-sourced pairwise preference leaderboard across open and closed models.
- τ-bench — Tool-augmented agent benchmark on retail & airline customer-service tasks.
- MMLU — Massive Multitask Language Understanding — 57 academic and professional subjects.
These are the benchmarks whose task shape bears on this workflow. Each one only describes its own protocol and date, so confirm the harness matches your workload before treating a score as evidence.
Continue your research
Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to RAG Support Bot — Canonical Workflow.