Knowledge Base

A knowledge base is a structured collection of information that AI agents can search, reference, and use to provide accurate, domain-specific answers. It's the "brain" that makes an AI agent an expert in your field rather than a generic chatbot.

Knowledge base content types include: documents (PDFs, Word docs, text files), web pages (crawled website content), FAQs (question-answer pairs), audio/video (transcribed and indexed), spreadsheets (structured data), and APIs (real-time data connections).

How knowledge bases work with AI agents: content is uploaded and chunked into smaller passages, each chunk is converted to an embedding vector (numerical representation), vectors are stored in a vector database for fast retrieval, when a user asks a question, the most relevant chunks are retrieved, and the AI uses these chunks as context to generate accurate, grounded answers.

This process is called Retrieval-Augmented Generation (RAG), and it's the most effective way to make AI agents accurate and domain-specific without expensive fine-tuning.

Best practices for knowledge bases: keep content current (outdated information leads to wrong answers), write clearly (AI retrieves better from well-written content), organize logically (consistent structure improves retrieval), cover common questions (anticipate what users will ask), and monitor gaps (use analytics to find questions the AI struggles with, then add content).

On Chipp, builders upload knowledge sources through a visual interface — documents, websites, audio, video, and more — and the platform handles chunking, embedding, indexing, and retrieval automatically.