RAG Developer for Business: AI on Your Data

RAG stands for Retrieval-Augmented Generation. Before asking a large language model to answer, the system retrieves relevant passages from a knowledge base and adds them to the model context. Google Cloud describes the flow as retrieval, prompt augmentation and grounded generation.

This architecture fits procedures, catalogs, contracts, tickets and internal documentation that changes faster than a model's training data. See Google Cloud's RAG definition.

A support chatbot retrieves the procedure that matches a question, cites the passage and escalates when no adequate source is found. An internal assistant lets employees query manuals, contracts or ticket history without searching several folders.

The same architecture can power product search, sales assistance or a multilingual French and Malagasy knowledge base. Source quality and access control matter more than the raw number of imported documents.

I inventory source formats, owners and update frequency. Documents are cleaned, split into coherent passages and indexed with metadata such as title, date, customer and access level.

At query time, hybrid search can combine semantic similarity with keywords. A reranker orders the candidate passages. The LLM receives only useful evidence and must answer from it or state that the information is unavailable.

A RAG pipeline can write a fluent answer from the wrong passage. I therefore measure two separate stages: whether retrieval found the expected source and whether the generated answer remains faithful to it. Test sets include normal questions, ambiguous wording, missing answers and permission boundaries.

Metrics cover passage relevance, groundedness, completeness, citations and correct refusal. This creates a baseline for improving sources, chunking, search and prompts.

Depending on the constraints, I use PostgreSQL with pgvector, Supabase, a managed search engine or a specialized vector database. The API can run in Python with FastAPI or inside a Next.js product. Claude, OpenAI or Gemini generate the answer while n8n orchestrates ingestion and updates.

The choice follows volume, update frequency, filters, latency and control requirements. AWS guidance on production RAG components also separates embeddings, storage and retrieval concerns.

The index must enforce the same permissions as the original documents. API keys remain server-side, sensitive content is excluded from unnecessary logs and deletions propagate to the index. Responses expose sources so users can verify them.

I start with one bounded corpus, measure failures and expand gradually. When the system must also act in business tools, RAG becomes one component of an AI integration, with human approval for sensitive actions.