AI Knowledge Base / RAG
Retrieval-Augmented Generation – an AI approach where language models use company-specific documents to give accurate, factual answers.
Large language models like GPT are powerful but don’t know your company’s data. RAG (Retrieval-Augmented Generation) closes that gap: it combines an LLM’s language ability with your documents, manuals and databases. Result: an AI assistant that gives precise, up-to-date, source-based answers about your business.
What is AI Knowledge Base / RAG?
RAG (Retrieval-Augmented Generation) is an AI architecture that combines retrieval (search in a knowledge base) and generation (answer generation by an LLM from retrieved content). Instead of relying only on the LLM’s training knowledge, each query retrieves relevant passages from your documents and passes them to the LLM as context. The LLM then answers only from those sources – not from general training. Answers are current, factual and citable.
How does AI Knowledge Base / RAG work?
RAG in four steps: 1) Indexing: Documents (PDF, Word, wiki) are split into chunks, turned into vectors by an embedding model and stored in a vector DB (e.g. Pinecone, Weaviate, Chroma). 2) Retrieval: The user query is also embedded; similarity search (e.g. cosine) finds the most relevant chunks. 3) Augmentation: Those chunks are sent to the LLM with the query and an instruction to answer only from them. 4) Generation: The LLM produces a natural-language answer and ideally cites sources.
Practical Examples
Internal knowledge portal: Staff ask the AI about policies, leave rules or technical guides and get precise answers with document references.
Technical support: A customer chatbot has access to full product docs, FAQ and release notes and answers complex technical questions.
Legal: Lawyers ask about contract terms, regulations or precedents – the RAG assistant searches thousands of documents and returns relevant excerpts.
Sales enablement: Sales ask for product comparisons, competitor info or case studies and get tailored answers from the knowledge base.
Typical Use Cases
Internal knowledge: Single AI access to company docs, policies and manuals
Customer support: Chatbots with access to full product documentation
Onboarding: New hires get instant answers on processes and tools
Research: Fast information retrieval from large document sets
Compliance: AI-assisted checks against regulatory requirements
Advantages and Disadvantages
Advantages
- Up to date: Uses current company documents, not stale training data
- Factual: Answers grounded in retrieved sources, not LLM hallucination
- Citable: Users can check and verify sources
- Data control: Company data stays in your systems, not in LLM training
- Cost-effective: No expensive fine-tuning; new docs are just indexed
Disadvantages
- Document quality: RAG is only as good as the underlying content
- Chunking: How documents are split strongly affects answer quality
- Latency: Retrieval + generation is slower than pure LLM (often 2–5 s)
- Complexity: Vector DBs, embeddings and prompts need AI expertise
- Imperfect: Retrieval can still miss relevant information
Frequently Asked Questions about AI Knowledge Base / RAG
RAG or fine-tuning?
What documents can I use for RAG?
What does a RAG knowledge base cost?
Related Terms
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