Key takeaways
- FAQs and knowledge base are the foundation. Add real conversations for phrasing.
- Product and policy data must be accurate and current.
- Include edge cases so the bot knows when to escalate.
A chatbot is only as good as what it's trained on. Generic training produces generic answers. To build something that actually helps your customers, you need the right data.
FAQs and knowledge base
Your existing FAQs, help articles, and internal docs. This is the foundation. Format them as Q&A pairs or structured content the model can retrieve.
Real conversations
Past support tickets, chat logs, and call transcripts. How do customers actually phrase questions? What do your best agents say? This grounds the bot in reality.
Product and policy data
Pricing, features, policies, availability. The bot needs to give accurate, up-to-date answers. Connect it to live data where possible.
Edge cases
The weird questions, the complaints, the things that go wrong. Train the bot to recognise when it's out of depth and escalate. Don't let it guess.