RAG_SYSTEMS
RAG Systems
RAG systems from Armenia for AI knowledge bases, document assistants and business teams that need answers grounded in trusted data.
For teams in Armenia, Yerevan, CIS and global markets
Built for business workflows, internal tools, integrations and AI-assisted operations.
Retrieval augmented generation system development
aicoding.am designs RAG systems that connect LLMs to company documents, product data and operational context. The result is a practical AI assistant that retrieves before it answers, cites the right material when needed and avoids generic chatbot behavior.
Where This Helps
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.
What You Get
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 I Use
Technology used for RAG Systems
vector search, Postgres, OpenAI, Claude, Gemini, embeddings, document pipelines.
Frequently Asked Questions
Practical answers about RAG Systems
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.