Build a Knowledge Base with AI
A knowledge base lets your team or customers find answers from your documents. AI-powered search and RAG make it smarter than keyword search. This guide covers platform choice, document ingestion, AI search, RAG setup, maintenance, and the build vs. buy decision.
Choosing a Platform
Managed — Notion, Confluence, Coda with AI search. Or dedicated KB tools like Guru, Slite, Document360. Fast to set up. Less control.
RAG platforms — CustomGPT, Chatbase, or similar. You upload docs; they provide a chatbot or search. Good balance of control and ease.
Custom build — Embeddings plus vector DB plus LLM. Full control. Requires development. Use when off-the-shelf does not fit.
Start managed or RAG platform. Build custom only when you have specific requirements.
Document Ingestion
Sources: PDFs, Word docs, markdown, Confluence, Notion, Google Docs. Most tools support multiple formats. Documents are split into chunks for retrieval. Default chunking often works. Tune if results are poor: smaller chunks for precision, larger for context. Add new docs as they are created. Set up sync from source systems when possible. Stale docs lead to wrong answers.
AI-Powered Search
Semantic search finds by meaning, not just keywords. How do I reset my password matches docs about account recovery even without those words. How it works: embeddings plus vector search. Query is embedded; similar chunks are retrieved. Optionally, an LLM generates an answer from retrieved chunks (RAG). Built into many KB platforms. Or use a vector DB (Pinecone, Weaviate) plus embedding API plus LLM for custom.
RAG Setup for Internal Docs
RAG retrieves relevant chunks, feeds them to the LLM, generates an answer grounded in your docs. Reduces hallucinations. Setup: ingest docs, embed, store in vector DB. At query time: embed query, retrieve top k chunks, prompt LLM with chunks, return answer. Tuning: adjust chunk size, retrieval count (k), and prompt. Answer only from the provided context. If unsure, say so.
User-Facing vs. Internal Knowledge Bases
Internal — For employees. Can include confidential or draft content. Access control by team or role.
User-facing — For customers. Public help center, FAQ. Only curated, approved content. Different access and compliance requirements.
Many platforms support both. Separate instances or access controls.
Maintenance and Updating
Regular updates — Add new docs. Remove outdated ones. Re-index when you make bulk changes.
Quality checks — Periodically test queries. Are answers correct? Update chunks or prompts when they are not.
Feedback loop — Was this helpful? Use feedback to find gaps and improve content.
The Build vs. Buy Decision
Buy when — You need it fast. Standard use case. No unique requirements. Team has no ML capacity.
Build when — You need on-prem, custom UX, or integration with proprietary systems. You have ML or engineering capacity. Off-the-shelf does not fit.
Hybrid — Use a platform for most; custom components for specific needs.
How This Connects to Hokai
The >Model Directory includes KB and RAG tools. >Smart Match for knowledge base or internal docs returns options. >What Is RAG? explains the underlying tech. >Build a Support Chatbot uses a KB for chatbots.
The Bottom Line
Choose a platform (managed, RAG, or custom). Ingest and structure your docs. Use semantic search and RAG for better answers. Maintain and update regularly. Build vs. buy depends on speed, requirements, and capacity. Start with a platform; customize as needed.