RAG Development for AI That Can Use Your Business Knowledge.
Retrieval-Augmented Generation, or RAG, allows AI applications to retrieve relevant information from approved knowledge sources before generating a response. Primo Coding builds RAG-based knowledge systems that help AI work with company documents, data and domain-specific information.
What Is RAG?
Retrieval-Augmented Generation is an AI architecture that retrieves relevant information from external knowledge sources and supplies that information to a language model as context for a response. This can help an application answer questions using current or proprietary information rather than relying only on knowledge contained in the underlying model.
What Can You Build With RAG?
Internal Knowledge Assistants
Help employees search policies, documentation and institutional knowledge conversationally.
Document Search
Retrieve relevant information from large collections of documents based on meaning, not only exact keywords.
Customer Support Knowledge
Help service teams or customers find grounded answers from approved support content.
Technical Documentation Assistants
Search manuals, product information and technical documentation.
Product & Project Knowledge
Give AI access to structured information about products, projects or services.
RAG-Enabled Applications
Use retrieval as one component inside a larger AI application or workflow.
How a RAG Solution Works
Ingest
Collect and prepare approved documents or other knowledge sources.
Chunk
Divide content into useful sections for retrieval.
Embed
Convert content into numerical representations that capture semantic meaning.
Store
Save embeddings and related information in a vector database or retrieval system.
Retrieve
Find information relevant to the user’s question or task.
Generate
Provide retrieved context to the language model so it can produce a grounded response.
Frequently Asked Questions
Retrieval-Augmented Generation, or RAG, is an AI architecture that retrieves relevant information from external knowledge sources before generating a response. It allows an AI application to use company documents, databases or other approved information as context.
A vector database stores numerical representations of content called embeddings. These representations allow applications to retrieve information based on semantic similarity, meaning the system can find content related to the meaning of a question even when the wording is different.
RAG supplies an AI application with relevant external information when a request is made. Fine-tuning modifies model behavior using additional training examples. RAG is often appropriate when an application needs access to current or proprietary knowledge, while fine-tuning may be appropriate when the goal is to change how a model performs a particular task.
Yes, depending on the architecture and access controls. A RAG application can be designed to retrieve from approved company knowledge sources without making those documents publicly available.
RAG can improve grounding by providing relevant source information, but it does not guarantee that every response will be correct. Evaluation, retrieval quality, prompting, model choice and application safeguards still matter.
Practical RAG Experience
Primo Coding’s AI team has worked with RAG pipelines that include document ingestion, chunking, embeddings, Chroma vector databases, semantic retrieval and retrieval-gate logic. The team has applied RAG across chat-agent, voice-agent and AI content-platform development work.