40 lines
2 KiB
Markdown
40 lines
2 KiB
Markdown
---
|
|
type: source
|
|
source_type: research-question
|
|
title: "Research: Telegram bot best practices for community knowledge ingestion"
|
|
date: 2026-03-21
|
|
domain: ai-alignment
|
|
format: research-direction
|
|
status: processed
|
|
proposed_by: "@m3taversal"
|
|
contribution_type: research-direction
|
|
tags: [telegram-bot, community-management, knowledge-ingestion]
|
|
---
|
|
|
|
# Research Question: Telegram Bot Best Practices for Community Knowledge Ingestion
|
|
|
|
## What we want to learn
|
|
|
|
Best practices and strategies for AI-powered Telegram bots that operate in crypto/web3 community groups. Specifically:
|
|
|
|
1. How do successful community bots decide when to speak vs stay silent in group chats?
|
|
2. What are proven patterns for bots that ingest community knowledge (claims, data points, corrections) from group conversations?
|
|
3. How do other projects handle the "tag to get attention" vs "bot monitors passively" spectrum?
|
|
4. What engagement patterns work for bots that recruit contributors (asking users to verify/correct/submit information)?
|
|
5. How do projects like Community Notes, Wikipedia bots, or prediction market bots handle quality filtering on user-submitted information?
|
|
|
|
## Context
|
|
|
|
We have a Telegram bot (Rio/@FutAIrdBot) deployed in a 3-person test group. The bot responds to @tags with KB-grounded analysis and can search X for research. We want to deploy it into larger MetaDAO community groups (100+ members).
|
|
|
|
Key tension: the bot needs to be useful without being noisy. In testing, it responded to messages not directed at it (conversation window auto-respond). We stripped that and now it only responds to @tags and reply-to-bot.
|
|
|
|
The next evolution: other users can tag the bot when they see something interesting ("@FutAIrdBot this is worth tracking"). This makes the community the filter, not the bot.
|
|
|
|
## What to search for
|
|
|
|
- Telegram bot engagement strategies in crypto communities
|
|
- AI agent community management best practices
|
|
- Knowledge ingestion from group chats
|
|
- Community-driven content moderation/curation bots
|
|
- Prediction market community bot patterns (Polymarket, Metaculus)
|