June 11, 2026/MobAppAI Engineering Team/6 min read
The problem RAG solves
A language model only knows what it saw during training. It has no access to your internal documents, its knowledge has a cutoff date, and when it doesn't know something it can produce confident, wrong answers. RAG addresses all three by giving the model your data to work from at answer time.
How RAG works, in four steps
- Index your content: documents and records are split into chunks and stored in a searchable vector index.
- Understand the query:the user's question is converted into the same vector representation.
- Retrieve: the system pulls the most relevant chunks for that question.
- Generate: the model writes an answer using those chunks as context, and can cite where each fact came from.
When to use RAG
- Answers must reflect current or frequently-changing data.
- Responses need to be grounded in private, internal knowledge.
- You need citations and an audit trail for trust or compliance.
RAG vs fine-tuning
RAG gives a model knowledge; fine-tuning teaches it a skill or style. They are complementary, and many systems use both — we break down the choice in RAG vs fine-tuning. Ready to build? See our RAG implementation services.