Workflow
Build an Internal Knowledge Bot
Ship a Slack bot that answers questions from your company docs.
Problem
Team members repeatedly ask the same questions that are already answered somewhere in Notion, Drive, or Confluence.
Solution
Ingest your sources into a vector store and expose a RAG-powered chat surface inside Slack.
Steps
- 01Inventory the source systems and decide what is in-scope.
- 02Set up ingestion + chunking for each source.
- 03Embed chunks and store them in a vector database.
- 04Wire a retrieval + grounded-answer prompt into a chat endpoint.
- 05Deploy a Slack slash command pointing at the endpoint.
Tools Used
Prompts Used
Related Dictionary
↳ connected nodes
Dictionary↳ linked
RAG (Retrieval-Augmented Generation)
Inject external knowledge into an LLM at query time.
Dictionary↳ linked
Vector Database
A database optimized for similarity search over embeddings.
Tool Stack↳ linked
RAG Starter Stack
Minimum viable stack to ship a production RAG chatbot.
Prompt↳ linked
Grounded Answer Prompt
Force the model to answer only from provided sources, with citations.
Comparison↳ linked
RAG vs Fine-Tuning
When to retrieve, when to retrain.
Comparison↳ linked
Chatbot vs AI Agent
Conversational interface vs autonomous executor.
Use Case↳ linked
SaaS Co. Cuts Support Tickets 40% With Internal Bot
A 50-person SaaS deployed a RAG bot over their help center and internal wiki.
Use Case↳ linked
E-commerce Brand Automates 70% of Support Tickets
A DTC brand deployed a RAG support agent over policies, FAQs, and order data.
Dictionary↳ linked
Semantic Search
Finding information by meaning rather than exact keyword match.
Comparison↳ linked
Vector Database vs Knowledge Graph
Similarity retrieval versus explicit relationship mapping.
Use Case↳ linked
Support Team Replaces Wiki Sprawl With a Knowledge Graph
A support org connected policies, playbooks, tickets, and RAG answers into one system.