Garbage In, Hallucinations Out
Retrieval-Augmented Generation (RAG) is the foundational technology powering modern customer service AI agents. However, feeding unorganized raw spreadsheets or conflicting PDF brochures leads to confusing customer responses.
To guarantee precise, hallucination-free answers, your knowledge base should be structured into clean, modular question-and-answer pairs and clear policy documentation.
3 Rules for Structuring AI-Ready Knowledge Bases
Follow these core formatting principles when uploading business documentation:
- One Concept Per Entry: Separate refund policies from shipping guidelines so semantic vector embeddings isolate the exact answer.
- Include Concrete Pricing & Constraints: State currency, timelines (e.g. '14 business days'), and eligibility criteria explicitly.
- Regular Version Updates: Archive outdated promotions and update contact details to maintain strict accuracy.
Semantic Vector Search in Flowcore
Flowcore automatically embeds your knowledge base using OpenAI embeddings stored in PostgreSQL with pgvector. When a WhatsApp user asks a question in natural language, our engine retrieves the top relevant context snippets before prompting the agent, ensuring factual and helpful replies 24/7.