RAG Systems & AI Assistants

RAG (Retrieval-Augmented Generation) is a technique that connects a large language model (like GPT or Claude) to a vector database containing your own documents. When a user asks a question, the system first searches your documents for the most relevant content, then uses the LLM to generate an accurate, cited answer from that content, not from its training data. This eliminates hallucinations because the AI only answers from your verified information.

A regular AI chatbot uses an LLM's general training data to answer questions, it can hallucinate or give outdated answers. A RAG chatbot searches your own documents first, then generates an answer from that specific content with citations. The result is dramatically higher accuracy, verifiable answers, and responses that stay current as your documents update. We build RAG chatbots that can handle hundreds of thousands of documents with sub-second retrieval.

We build RAG systems that process PDFs, Word documents, PowerPoint files, web pages, databases, CSV files, API responses, Notion pages, Confluence wikis, SharePoint libraries, and more. Our LongParser open-source tool handles complex document layouts including tables, images, and multi-column content that standard parsers miss.

We implement multiple layers of hallucination prevention: strict retrieval grounding (the LLM can only use retrieved content), citation enforcement (every answer must cite its source), confidence scoring (low-confidence answers trigger fallback responses), and production monitoring with LongTracer, our open-source hallucination detection tool. We also run regression testing with LongProbe to catch accuracy regressions before deployment.

A focused RAG system, document ingestion pipeline, vector database, retrieval API, and chat interface, typically takes 4–8 weeks to production. Timeline depends on document volume, required accuracy thresholds, integration complexity (CRM, SSO, existing tools), and whether you need a custom UI or an embeddable widget. We deliver working builds every 2 weeks via agile sprints.

Fine-tuning trains the LLM itself on your data, it bakes knowledge into the model weights. RAG keeps the LLM unchanged and retrieves relevant content at query time. For most business use cases, RAG is better: your knowledge base stays up-to-date without retraining, answers are traceable to source documents, and it's far less expensive. Fine-tuning is best for changing the model's tone, style, or domain vocabulary, not for knowledge grounding.