RAG engineer: the profile behind AI systems connected to business data

Generative artificial intelligence models can answer questions, summarize information, and generate content using the knowledge acquired during their training. However, that knowledge does not automatically include contracts, procedures, technical documentation, catalogs, internal policies, or private knowledge bases of a company.

To utilize that information, architectures such as Retrieval-Augmented Generation (RAG) emerge. Its function is to retrieve relevant data from external sources and provide it to the model as context before generating a response.

The approach has a significant advantage for organizations: it allows the construction of AI applications supported by business knowledge without relying solely on what the model learned during its training. AWS describes RAG as a mechanism to enhance the capabilities of large language models through specific data sources without the need to retrain them.

As these architectures become more sophisticated, the need for professionals capable of designing them also increases. The RAG engineer works precisely at that intersection of artificial intelligence, information retrieval, data, and software development.

What is RAG and why is it useful for businesses

Retrieval-Augmented Generation combines two processes: retrieval and generation. Before asking the model to produce a response, the system searches for information related to the query within a specific knowledge source.

Imagine a company with thousands of technical documents. When an employee asks a question, the system identifies which fragments contain relevant information, retrieves that content, and incorporates it into the context sent to the model. The response can then be constructed using specific information from the organization.

The process allows for the development of internal assistants, intelligent search engines, support systems, tools for analyzing documentation, and applications capable of querying corporate knowledge using natural language.

The artificial intelligence specialists working on these solutions must ensure that models and data work together. The quality of the result depends on both the capability of the LLM and the information that the system can retrieve.

What a RAG engineer does

A RAG engineer designs, implements, and optimizes the components responsible for connecting AI models with external knowledge sources.

Their work begins long before sending information to an LLM. Documents must be located, processed, structured, and indexed. Then, it is necessary to develop mechanisms capable of retrieving the appropriate content for each query and evaluating whether it truly provides the context that the model needs.

Some of their most common responsibilities include:

• Designing ingestion pipelines, connecting documents, knowledge bases, and other business sources.

• Defining chunking strategies, determining how to divide information without losing relevant context.

• Generating and managing embeddings to semantically represent documents and queries.

• Implementing retrieval mechanisms, including semantic search, keyword search, or hybrid strategies.

• Applying reranking when it is necessary to reorganize results according to their relevance.

• Evaluating the RAG system, differentiating retrieval problems from generation problems.

• Implementing permissions and access controls to prevent the model from receiving information that the user should not consult.

• Optimizing performance and costs, considering latency, storage, retrieval, and model consumption.

Designing ingestion pipelines, connecting documents, knowledge bases, and other business sources.

Defining chunking strategies, determining how to divide information without losing relevant context.

Generating and managing embeddings to semantically represent documents and queries.

Implementing retrieval mechanisms, including semantic search, keyword search, or hybrid strategies.

Applying reranking when it is necessary to reorganize results according to their relevance.

Evaluating the RAG system, differentiating retrieval problems from generation problems.

Implementing permissions and access controls to prevent the model from receiving information that the user should not consult.

Optimizing performance and costs, considering latency, storage, retrieval, and model consumption.

These responsibilities show why RAG should not be reduced to merely storing embeddings in a vector database. Information retrieval constitutes a complete system that requires design, evaluation, and maintenance.

How a RAG architecture works

A RAG architecture can be conceptually divided into two major moments: knowledge preparation and query execution.

During preparation, data is extracted from its original sources. It can come from PDFs, documents, internal pages, content management systems, databases, or corporate applications. The content is cleaned and divided into units that can be retrieved later.

Then, vector representations or embeddings are generated. These vectors allow for the mathematical comparison of the semantic relationship between a query and different fragments of information.

When a question arrives, the system generates a representation of that query and searches for related content. The selected results are incorporated into the context received by the model, along with instructions on how to use them.

The LLM ultimately generates a response based on the query and the retrieved knowledge. In more advanced applications, filters, reranking, hybrid search, query expansion, and other mechanisms can be added to improve accuracy.

How to improve retrieval quality in a RAG architecture

Chunking can completely change the outcome

Dividing documents may seem like a simple task, but it has a considerable influence on the quality of retrieval.

If the fragments are too small, they may lose the necessary context to correctly interpret information. If they are excessively large, they may incorporate irrelevant content and unnecessarily consume the model's context window.

A technical manual, legislation, a product sheet, and a support conversation do not necessarily have the same structure. Using a single strategy for all documents can produce inconsistent results.

The RAG engineer analyzes the nature of each source and determines how to process it. They may use fixed size, paragraph separation, semantic structure, headers, or specific strategies for certain formats.

This phase maintains a direct relationship with data engineering. When the organization works with large volumes of information from different systems, data engineers can take care of building reliable pipelines that keep knowledge available and up to date.

Embeddings, semantic search, and hybrid retrieval

Embeddings allow texts to be represented through numerical vectors that capture certain semantic relationships. This makes it possible to retrieve documents conceptually related to a query even if they do not use exactly the same words.

A traditional search based solely on keywords may struggle when the user and document use different vocabulary. Vector search tries to approximate the meaning of both contents.

This does not mean that semantic search is always superior. Certain identifiers, product names, codes, references, or very specific terms work particularly well with lexical search.

For this reason, many architectures use hybrid search, combining semantic and traditional signals. The RAG engineer evaluates which works best according to the type of information and the actual queries from users.

The role of vector databases

Vector databases are closely associated with RAG because they allow for the storage of embeddings and efficient similarity searches.

However, the technological choice depends on the scale and existing infrastructure. Some organizations use engines specifically designed for vectors, while others incorporate vector capabilities within databases or search platforms that are already part of their stack.

Microsoft notes that an enterprise RAG solution needs a search strategy capable of finding the most relevant content and may use vector, textual, or hybrid search depending on the architecture.

For a RAG engineer, knowing database architectures is useful because they must consider indexing, filters, performance, availability, updating, and access control, in addition to vector similarity.

Retrieval and reranking: finding information is not enough

One of the biggest challenges of RAG is to retrieve exactly the context that the model needs.

A search engine may find twenty fragments related to a query, but only some contain the necessary information to answer it correctly. Sending all results to the LLM increases noise, context consumption, and cost.

Reranking adds a second evaluation stage. After retrieving an initial set of candidates, another mechanism analyzes their relationship with the query and reorganizes the results.

This architecture allows for a relatively broad initial search and then selects a smaller group of contents with a higher likelihood of being useful.

The combination of retrieval, filters, and reranking can have more impact on the final quality than simply switching from one language model to a more powerful one.

Evaluation, reliability, and security of a RAG system

Evaluating a RAG system requires separating retrieval and generation

When an application responds incorrectly, it is necessary to identify where the failure occurred.

The model may have received the correct information and misinterpreted it. The opposite can also happen: the LLM could have responded correctly, but the system never retrieved the appropriate document.

Therefore, evaluation must analyze both stages.

In retrieval, aspects such as the presence of the relevant document among the retrieved results or its position can be measured. In generation, fidelity to context, relevance of the response, and the ability to avoid unsupported claims from the provided sources can be evaluated.

This approach allows for optimizing the correct component. Increasing the model size will hardly resolve a problem caused by poorly processed documents or a deficient retrieval strategy.

RAG does not automatically eliminate hallucinations

One of the reasons to implement RAG is to provide the model with verifiable and up-to-date information. However, connecting an LLM with corporate documents does not guarantee that all responses will be correct.

The system may retrieve irrelevant, contradictory, or outdated information. It may also happen that the generated response does not faithfully reflect the retrieved content.

The architecture needs evaluation mechanisms and rules that indicate how the model should behave when it does not have sufficient information. In certain applications, it may be preferable to respond that no evidence is available rather than completing a response using general knowledge.

Traceability is also important. When the user can identify which documents support a response, it becomes easier to verify the information and detect problems in the knowledge base.

Security and permissions in systems connected to business information

RAG provides artificial intelligence with access to knowledge that may include sensitive information. The architecture must maintain the same restrictions that exist in the original systems.

An employee authorized to consult commercial documentation should not obtain confidential information from human resources simply because both contents are indexed on the same platform.

Access controls can be applied during retrieval using metadata, filters, and user identity. It is also necessary to control what information is sent to external model providers and under what conditions it is processed.

AWS recommends applying authentication, authorization, and filtering mechanisms to ensure that generative systems retrieve only information accessible to each user.

Security must be part of the pipeline from the design stage. Adding permissions after centralizing large amounts of knowledge can be considerably more complex.

RAG engineer vs data engineer vs AI engineer vs machine learning engineer

RAG is situated in an area where several disciplines converge, so responsibilities can be distributed differently depending on the company.

In small teams, an AI engineer may take on much of the RAG responsibilities. In organizations with large document volumes, complex search requirements, or multiple applications using the same knowledge layer, specialization becomes more valuable.

There may also be collaboration with machine learning engineers when the architecture requires specific models for embeddings, classification, ranking, or evaluation.

RAG and AI agents: from knowledge to action

AI agents expand the role of RAG. An assistant can retrieve information to answer a question; an agent can use that knowledge to decide what action to take next.

For example, a support agent can consult technical documentation, identify a procedure, and use a tool to execute an authorized operation. Another agent may analyze contracts, retrieve internal policies, and prepare an action that later requires human approval.

In these architectures, RAG functions as a knowledge layer. The agent needs to find correct information before using it during its decision-making process.

There are also emerging approaches known as Agentic RAG, where the system itself decides which sources to consult, reformulates searches, and performs several retrieval stages before generating a response or executing an action.

This evolution brings RAG closer to software architecture, because retrieval, models, tools, applications, and controls must be integrated within a maintainable system.

When does your company need a RAG architecture and a specialized profile

6 signs that your company needs a RAG architecture

Not all artificial intelligence applications need Retrieval-Augmented Generation. A tool used solely for creative tasks or general knowledge can work perfectly without accessing corporate information.

RAG starts to make sense when needs like these arise:

• Users need to query internal documentation using natural language.

• Responses must use updated information that is not part of the model's original training.

• The organization has large volumes of knowledge dispersed among documents, applications, and repositories.

• It is important to show the sources used to support certain responses.

• Different users have different permissions, so retrieval must respect access controls.

• Multiple applications or agents need to use a common corporate knowledge base.

Users need to query internal documentation using natural language.

Responses must use updated information that is not part of the model's original training.

The organization has large volumes of knowledge dispersed among documents, applications, and repositories.

It is important to show the sources used to support certain responses.

Different users have different permissions, so retrieval must respect access controls.

Multiple applications or agents need to use a common corporate knowledge base.

When several of these situations coincide, directly connecting documents to a model often proves insufficient. The company needs to design a knowledge layer that can be maintained, evaluated, and scaled.

When does your company need a specialized RAG engineer

During a proof of concept, an AI engineer can build an initial implementation using managed services and a limited knowledge base. This allows for quickly validating whether access to corporate information improves the use case.

The need for specialization increases as document volume grows, different types of sources emerge, or retrieval accuracy becomes an important requirement. It also increases when there are complex permissions, multiple languages, frequently changing information, or several applications consuming the same knowledge.

In these scenarios, small changes in chunking, search, filters, or ranking can significantly modify performance. The architecture requires continuous evaluation rather than a one-time configuration.

The RAG engineer provides depth precisely in that layer. Their goal is to ensure that the right information reaches the model at the right time and under the correct access conditions.

RAG is evolving into a knowledge infrastructure for AI

The early implementations of Retrieval-Augmented Generation primarily focused on connecting documents with chatbots. Current architectures are significantly expanding that scope.

A single knowledge layer can feed assistants, search engines, internal copilots, agents, and functionalities integrated into different applications. This makes RAG a reusable component within an organization's artificial intelligence infrastructure.

It also increases the sophistication of retrieval. Hybrid search, reranking, graph-based retrieval, and agentic strategies allow for working with increasingly complex queries and sources.

The consequence is that the problem is no longer just about “connecting a PDF to an LLM.” Designing an enterprise RAG architecture involves managing knowledge, search, security, evaluation, integration, and continuous operation.

The RAG engineer connects artificial intelligence with the real knowledge of the company

Generative models provide powerful capabilities, but much of the business value appears when they can work with specific information from each organization.

RAG provides the mechanism to build that connection. The RAG engineer is responsible for ensuring that the process is accurate, scalable, and secure: from the preparation of documents to the retrieval of the context that the model ultimately receives.

As companies deploy more assistants and agents over internal information, this specialization may become increasingly important within artificial intelligence teams. The quality of the retrieved knowledge will directly condition the quality of many of those applications.

For organizations transitioning from experiments with LLMs to systems connected with business data, IT outsourcing models allow for incorporating specialized profiles according to the architecture and phase of the project. If you need to define what capabilities your implementation requires, you can schedule a call with lateam to analyze the project and the necessary technical profiles.