Answers
What is RAG (retrieval augmented generation)?
RAG, short for retrieval augmented generation, retrieves relevant documents from a knowledge base and feeds those documents to a language model before the model generates an answer. RAG grounds a language model's response in specific source material instead of relying only on training data.
01How does RAG work?
RAG works in two stages. First, a retrieval step searches a knowledge base, often using vector search, to find the documents most relevant to the question asked. Second, a generation step feeds those retrieved documents into a language model along with the question, and the model writes an answer grounded in that material.
The knowledge base is usually built by splitting source documents into chunks and converting each chunk into a vector representation stored for fast search later.
02Why do companies use RAG instead of fine tuning?
Companies use RAG because updating a knowledge base is faster and cheaper than retraining a model, and RAG lets a system cite the specific source used for an answer. Fine tuning changes the model itself and works best for adjusting tone or format, not for keeping facts current.
Many production systems combine both approaches, using RAG for current facts and light fine tuning for tone or task format.
03What are the main components of a RAG system?
A RAG system needs a document store, a chunking process that splits documents into searchable pieces, an embedding model that converts chunks into vectors, a vector database for search, and a language model that generates the final answer from retrieved chunks.
Some RAG systems add a reranking step that reorders retrieved chunks by relevance before passing them to the language model, which can improve answer accuracy.
04What problems does RAG solve?
RAG reduces made up answers, known as hallucination, by grounding a language model's response in retrieved source material rather than relying only on the model's training data. RAG also lets an organization answer questions about internal documents the model was never trained on.
RAG helps keep answers current, since updating the knowledge base does not require retraining the underlying language model.
05What are the limits of RAG?
RAG depends on the quality of the retrieval step. If the search returns the wrong or incomplete documents, the language model produces an answer grounded in the wrong material rather than the correct one. Poorly chunked documents or outdated knowledge bases also weaken results.
RAG adds latency compared to a model answering directly, since the retrieval step must run before generation can begin.
06Specification
| Spec | Detail |
|---|---|
| Typical cost | RAG implementation projects typically run from the low tens of thousands of dollars depending on document volume and integration count. |
| Typical timeline | Industry timelines for a first RAG deployment typically run four to eight weeks. |
| Prerequisites | A defined document set, access to source systems holding those documents, and a chosen vector database. |
| Common failure modes | Poor document chunking, outdated knowledge bases, and no process for reviewing retrieval accuracy over time. |
| Who it suits | Organizations with a large, changing body of internal documents that a language model needs to reference accurately. |
07RAG versus fine tuning
| Aspect | RAG | Fine tuning |
|---|---|---|
| Update speed | Fast, update the document store | Slower, requires retraining |
| Best for | Current facts and cited sources | Tone, format, and task style |
| Setup complexity | Moderate, needs a retrieval pipeline | Lower for small adjustments, higher for large changes |
08How to build a RAG system
- Step 01
Collect and clean documents
Gather the source documents the system should reference and remove outdated or duplicate content.
- Step 02
Chunk and embed
Split documents into consistent chunks and convert each chunk into a vector for search.
- Step 03
Set up retrieval
Configure a vector database and test retrieval accuracy against a sample set of questions.
- Step 04
Connect generation and test
Feed retrieved chunks into a language model, then test answer accuracy before full deployment.
FAQQuestions
Related questions
Does RAG eliminate hallucination completely?
No. RAG reduces hallucination by grounding answers in retrieved documents, but a language model can still misread or misstate retrieved content, so review remains important for high stakes answers.
Can RAG work with a small document set?
Yes. RAG can work with a small document set, though the benefit grows with larger, changing knowledge bases where retraining a model would be impractical.
Is RAG the same as a search engine?
No. A search engine returns a list of documents. RAG uses a search step to retrieve documents, then a language model generates a direct answer using that retrieved material.
How often should a RAG knowledge base be updated?
Most organizations update a RAG knowledge base as source documents change, ranging from daily updates for fast changing data to monthly for stable reference material.
NEXTWhere to go next
AI integration is the Lingows AI page that covers this work in delivery detail, including scope, timeline, and how the build is run.
Last reviewed 2026-08-16