RAG —retrieval-augmented generation— is a technique that connects a language model with your company’s own data. Before answering, the system searches your sources for the most relevant pieces of information and hands them to the model as context. The result is an answer grounded in your own, up-to-date, verifiable information instead of only the general knowledge the model was trained on.
What problem does RAG solve?
A language model knows a lot about the world, but it knows nothing about your company: it does not know your policies, your contracts, your catalog, or your internal manuals. And when asked about something it does not know, it sometimes answers confidently but incorrectly.
RAG closes that gap without touching the model. Instead of asking it to remember, we hand it the right information at the right moment: the system retrieves the relevant passages from your documents and the model writes the answer leaning on them. You get answers with your information, the ability to cite the source, and far less room to make things up.
How RAG works, step by step
RAG combines two moments: preparing your data once and, afterward, answering each query by leaning on it.
Preparation (once, then updatable):
- Chunking: your documents are split into manageable passages.
- Embeddings: each passage is turned into a numeric representation of its meaning.
- Index: those vectors are stored in a database designed to search by similarity.
Answering (on each question):
- Retrieval: the user’s question is also turned into an embedding, and the system finds the passages closest in meaning.
- Augmentation: those passages are added to the instruction the model receives, as context.
- Generation: the model writes the answer leaning on that context, and can point to where it came from.
The role of embeddings
The heart of RAG is search by meaning, and embeddings are what make it possible. An embedding is a numeric representation of the meaning of a text: two sentences that mean something similar end up “close,” even if they use different words.
Thanks to this, a question like “how many vacation days am I entitled to?” can retrieve a passage from your manual about the “annual rest period,” even if it shares not a single word. That is the difference from a traditional search engine, which only finds exact term matches.
RAG versus retraining the model
There are two ways for a model to use your information, and they solve different needs.
| Retrain / fine-tune | RAG | |
|---|---|---|
| How it brings in your data | By changing the model’s weights | By retrieving it at answer time |
| Updating | Repeat the process each time | Just update the sources |
| Cost and time | High, requires retraining | Quick to launch |
| Traceability | Hard to know the origin | Lets you cite the source |
| Best for | Changing the style or base task | Answering with your own, changing data |
Put simply: retraining changes what the model is; RAG gives it the right context every time it answers. For most enterprise cases —answering with your own, frequently changing information— RAG is the natural starting point.
How RAG is built on AWS
AWS provides the components to take RAG to production with enterprise control:
- Generative AI with Amazon Bedrock: managed foundation models and Knowledge Bases that index your documents, generate embeddings, and retrieve context without you having to assemble the pipeline piece by piece.
- Your data as the source: the documents live in your own storage and feed the index, so the information stays yours.
- Security and governance: controls to define who accesses which data, a key condition in regulated environments.
- Observability: traces and metrics to understand what was retrieved and what each answer was based on.
This way, the system answers with your real data and with the safety guardrails a business needs.
RAG and AI agents
RAG and AI agents work together. An AI agent reasons about a goal and executes steps; RAG is the usual way that agent accesses its own, up-to-date information so its decisions are grounded in real data. And when the agent needs to connect to external tools and sources in a uniform way, it often relies on standards like the Model Context Protocol (MCP).
Business benefits of RAG
- Answers with your information: the model leans on your up-to-date, specific documents.
- Traceability: each answer can point to its source, which builds trust and makes verification easier.
- Agile updates: keeping the system current is a matter of updating the sources, not retraining.
- Control of the data: the information stays in your environment, with the access rules you define.
When RAG makes sense
RAG adds the most value when you need AI to answer with your own knowledge that changes over time: internal knowledge bases, product support, policy questions, or technical documentation. For tasks that only require general knowledge, a model without retrieval may be enough and simpler to operate.
Like any capability, it is best to introduce RAG gradually, taking care of source quality, access permissions, and result measurement. Expert guidance helps define which cases are good candidates and how to govern them.
RAG as part of your AI strategy
Taking RAG to production is rarely an isolated experiment: it requires trustworthy data, security, and operations. At Caleidos we guide that journey within our generative AI and agents on AWS practice, with production cases documented in our case studies.
Frequently asked questions
What is RAG in simple terms? A technique that retrieves the relevant pieces of your data and hands them to a language model as context, so it answers with your information instead of only its general knowledge.
How is it different from retraining the model? Retraining changes the model with your data and must be repeated when it changes; RAG leaves the model untouched and adds fresh context at answer time, retrieving it from your sources.
How is it built on AWS? On Amazon Bedrock, with Knowledge Bases that index your documents, generate embeddings, and retrieve context, plus security and observability to operate it with enterprise control.
Considering bringing RAG into your operation?
Let’s talk about your case and we’ll give you a concrete recommendation on where to start with RAG on AWS.
