The problem: AI that makes things up

Large Language Models have a critical flaw: they generate confident-sounding responses that are sometimes completely false. For businesses, this creates real risks—a support chatbot inventing refund policies, an HR assistant citing procedures that don't exist.

The solution is RAG (Retrieval-Augmented Generation): an architecture that retrieves relevant official documents and supplies them as context for an answer.

How document ingestion works

Before your AI can answer questions, your documents must be indexed in a Vector Store. Here is the n8n workflow:

The process is straightforward:

Document Upload: Submit documents via form, upload, or webhook
Processing: Documents are split into searchable segments and cleaned
Embedding: Each segment is converted into a numerical representation using OpenAI
Storage: Vectors are stored in Supabase for instant retrieval

When a user asks a question, the system finds the most relevant segments and provides them to the AI as context—helping ground answers in your documentation.

How data retrieval works

Ingestion is only half the equation. When a user queries the system, the retrieval process ensures the AI receives the right context:

Query Embedding: The user's question is converted into the same vector format as your documents
Similarity Search: The system finds document segments with the closest semantic meaning
Context Assembly: Relevant segments are compiled and passed to the AI
Response Generation: The AI formulates an answer using only the retrieved context

Retrieval reduces reliance on model memory, but it cannot guarantee correctness. Source selection, prompt instructions, and evaluation all matter.

The role of prompt engineering

RAG retrieves the right information. The system prompt guides how the AI uses it:

Respond only with information from the provided context
Acknowledge when information is not available
Never make assumptions beyond the source material
Cite sources when relevant

Curated data and clear instructions help reduce unsupported answers. Test representative questions, missing information, and conflicting sources.

Business impact

Before RAG

  • AI invents plausible-sounding answers
  • No way to trace where answers come from
  • Business knowledge may not be available in the model
  • Legal and compliance risks

After RAG

  • AI receives relevant source material to support its answers
  • Full auditability and source attribution
  • New documents are searchable after successful indexing
  • Controlled, consistent information

Practical applications

Customer support

Chatbots that quote your actual policies, not invented ones.

Employee onboarding

New hires get accurate answers about real company procedures.

Sales enablement

Representatives access correct product specifications and pricing.

Compliance

Responses are traceable and auditable for regulatory requirements.

Getting started

The technical implementation requires a Vector Store (this example uses Supabase with pgvector), an embedding model (OpenAI), and an orchestration layer (n8n). Documents can be added incrementally; implementation depends on the content and workflow requirements.

Evaluate retrieval quality and response accuracy against your own use cases before relying on the system.