Capstone: Build a Company Knowledge Base Chatbot
The capstone brief
Build a complete RAG-powered chatbot that answers questions from a real set of company documents (policies, FAQs, product docs) — combining chunking, embeddings, vector storage, retrieval, and generation from every lesson in this path.
Assembling the full system
Chunk documents with semantic boundaries and overlap (Lesson 4) → embed and store in a vector DB (Lessons 2-3) → build the query pipeline with rewriting and tuned top-K (Lessons 5-6) → add hybrid search if your content has technical terms (Lesson 7) → evaluate against a test set (Lesson 8).
Deploying and maintaining it
Deploy the chatbot as a real interface (Slack bot, web widget, or internal tool), and set up the caching and re-indexing strategy from Lesson 10 so it stays cost-efficient and current as documents change.
Key Takeaways
- This capstone combines chunking, embeddings, retrieval, and generation from every lesson.
- Real company documents make this a genuinely useful, deployable tool.
- Evaluate against a test set before considering the chatbot production-ready.
- Ongoing caching and re-indexing keeps the system cost-efficient and current.
Ship your knowledge base chatbot
Build the complete RAG chatbot using a real set of your own documents, evaluate it against a test set, and deploy it as a working tool. Share it with the 404Fault community.