Grounding autonomous email replies in verified company documentation
Generic LLM auto-responders often invent policies, misquote refund windows, or provide outdated setup instructions. Retrieval-Augmented Generation (RAG) solves this by retrieving verified documentation snippets before generating the drafted response.
With LlamaIndex, engineering teams index internal Markdown documentation, OpenAPI specs, and knowledge bases to ground every generated customer response in factual data.
| System Attribute | Ungrounded Chatbot | LlamaIndex RAG + SadaSend |
|---|---|---|
| Factual Accuracy | Prone to hallucinations on specific pricing | 100% cited against retrieved vector chunks |
| Hallucination Risk | High on unknown domain edge cases | Low (gated by similarity threshold) |
| Outbound Security | Direct SMTP without allowlist | Scoped SadaSend key with approval mode |
| Escalation Path | Stalls or repeats hallucination | Automatic handoff to human support inbox |
Full RAG Ingestion and Email Dispatch Blueprint in Python
Here is a complete Python pipeline using LlamaIndex VectorStoreIndex and FunctionTool to answer incoming support inquiries with verified context:
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.tools import FunctionTool
from llama_index.core.agent import ReActAgent
from llama_index.llms.openai import OpenAI
import requests
import os
# 1. Load documentation into vector index
documents = SimpleDirectoryReader("./docs").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(similarity_top_k=3)
# 2. Define email dispatch tool with SadaSend
def dispatch_support_reply(recipient: str, subject: str, response_body: str) -> str:
"""Sends a verified customer support reply email."""
res = requests.post(
"https://api.sadasend.com/emails",
headers={
"Authorization": f"Bearer {os.getenv('SADASEND_API_KEY')}",
"Content-Type": "application/json",
"Idempotency-Key": f"reply_{hash(recipient + subject)}",
},
json={"to": recipient, "subject": subject, "text": response_body}
)
return "Dispatched" if res.status_code == 200 else f"Error: {res.text}"
email_tool = FunctionTool.from_defaults(fn=dispatch_support_reply)
# 3. Create RAG-powered agent
llm = OpenAI(model="gpt-4o")
agent = ReActAgent.from_tools([email_tool], llm=llm, verbose=True)
# Process customer inquiry
inquiry = "Customer Alex (alex@client.io) asks: 'How do I configure DMARC reporting on SadaSend?'"
agent.chat(f"Search our docs to answer: {inquiry}, then send the reply email.")Confidence gating & human escalation thresholds
In production, evaluate the retrieval score before generating the email. If the maximum cosine similarity across chunks is below 0.78, abort autonomous dispatch and route the ticket to a human support queue.
- Include explicit source citations in drafted emails so human reviewers can verify accuracy in seconds.
- Enforce per-key rate limits (e.g. max 40 emails/hour) to prevent runaway loops if an attacker spams the ingestion webhook.
- Store all drafted responses in PostgreSQL alongside customer feedback for continuous RAG index fine-tuning.
Building AI agents that send email?
Scoped API keys, per-key recipient allowlists, approval mode and a hosted MCP server with ten tools — on the free plan, without a card.