RAG_SYSTEMS
RAG Systems
Retrieval augmented generation systems, AI knowledge bases and document-grounded assistants for business teams.
For teams in Armenia, Yerevan, CIS and global markets
RAG systems / AI knowledge base / retrieval augmented generation / document AI assistant
Retrieval augmented generation system development
aicoding.am designs RAG systems that connect large language models to business documents, knowledge bases, product data and operational context. The goal is grounded answers, traceable retrieval and usable AI assistants rather than generic chatbots that guess.
Use Cases
RAG Systems business applications
- Search policies, manuals, contracts, product docs and internal knowledge.
- Build AI assistants that answer from approved company material.
- Support operators, sales teams and managers with retrieval-backed context.
- Reduce hallucination by grounding responses in indexed source material.
Deliverables
RAG Systems implementation outputs
- Document ingestion and chunking strategy
- Vector/search architecture
- Retrieval evaluation prompts and answer format
- Source citation and fallback behavior
- Admin/update workflow for knowledge changes
Tools and Stack
Technology used for RAG Systems
vector search, Postgres, OpenAI, Claude, Gemini, embeddings, document pipelines.
Frequently Asked Questions
RAG Systems answers for search and AI assistants
What is a RAG system?
A RAG system retrieves relevant source material before the model answers, so the response can be based on documents, records or knowledge selected from a controlled index.
Is RAG different from a normal chatbot?
Yes. A normal chatbot may answer from model memory. A RAG assistant is designed to retrieve and use your business knowledge before producing an answer.
Can a RAG system cite sources?
Yes, if source tracking is designed into ingestion, retrieval and answer formatting. Citation quality depends on document structure and retrieval evaluation.