Practical guides for teams building retrieval-augmented generation (RAG), document AI, and AI knowledge bases. Each guide targets common search questions and implementation topics so you can plan, build, and deploy faster.
New to RAG? Start with What is RAG? and Document AI & document understanding, then dive into the guides below.
End-to-end steps: ingest documents, chunk, embed, index, retrieve, and connect an LLM. Architecture checklist and common pitfalls.
Chunk size, overlap, structure-aware splitting, and how chunking affects recall and answer quality in retrieval systems.
What embeddings are, how they power semantic search, model choices, and tuning retrieval for your domain.
Semantic search vs keyword search, vector databases, similarity scoring, and when to add reranking.
Turn PDFs into searchable, Q&A-ready knowledge: parsing, tables, OCR considerations, and pipeline design.
Document Q&A and “chat with PDF” patterns: user experience, citations, and grounding answers in sources.
What to look for in AI-native knowledge bases: ingestion, permissions, multi-tenant RAG, and evaluation.
Data isolation, access control, audit trails, and deployment patterns for regulated or large organizations.