RAG vs Fine-Tuning: Which Should Your Business Use?

01 // The Problem

Standard LLMs are trained on public data up to a specific cutoff date. They lack access to internal business tables, real-time inventory listings, customer records, or pricing databases. Asking a base model about these details yields hallucinations—plausible but incorrect answers. Training a custom model from scratch is too expensive for most businesses, costing hundreds of thousands of dollars. Fine-tuning attempts to address this but cannot guarantee factual accuracy, which is unacceptable for customer-facing applications.

02 // The Context

Modern developers must decide how to inject private context. Retrieval-Augmented Generation (RAG) queries external databases for relevant documents and feeds them to the LLM as context. Fine-tuning, on the other hand, updates the actual weights of the neural network on a specific dataset. These two approaches serve different purposes: RAG acts as an open-book exam where the model reads the data directly, while fine-tuning is a closed-book exam where the model relies on pre-learned behaviors.

03 // The Solution

We advise businesses to use RAG for knowledge retrieval, factual lookups, and dynamically updating databases (like inventories or manuals). We restrict fine-tuning to tasks requiring custom style formatting, tone adaptation, or training on highly specific programming syntaxes. For complex systems, we often deploy a hybrid architecture: a fine-tuned model optimized for speed and structure, backed by a robust RAG vector database for factual information.

04 // System Architecture

05 // The Implementation

We configure a vector database (e.g. pgvector or Pinecone) containing chunked company documentation. When a query is sent, we convert it into a vector embedding using an embedding API, search the database for similar document chunks, and inject those chunks directly into the LLM system prompt. This guarantees that the LLM has access to the exact text required to formulate its response.

06 // Key Engineering Lessons

• RAG is significantly cheaper. RAG database lookups cost fraction of a cent, while fine-tuning runs require hours of expensive GPU compute time.
• RAG provides absolute data freshness. Updating a vector index takes milliseconds; retraining weights takes hours or days.
• RAG allows access controls. You can filter vector database queries based on user permissions, which is impossible with fine-tuned model weights.

07 // Technical Code Implementation

-- pgvector query to find relevant context chunks
SELECT id, content, 1 - (embedding <=> $1) as similarity
FROM document_chunks
WHERE 1 - (embedding <=> $1) > 0.75
ORDER BY similarity DESC
LIMIT 3;