RAG for enterprise: getting the AI to answer from your documents

What is enterprise RAG?

RAG (retrieval-augmented generation) is an architecture in which the AI searches the company's own data for information before answering [4]. It therefore answers from your documents rather than from its general knowledge alone [5]. Another formulation says that RAG "plugs" a language model into internal data instead of letting it answer from memory [33].

Two components combined

Technically, RAG combines an LLM (Large Language Model) and a document base to answer from the organization's data [28]. The model does not search on its own: a document retrieval step comes before the answer is written. Official French government sites frame its use in the same way: RAG can automate or assist tasks that require searching a document base, for example contracts [1].

AI knowledge base: definition

An AI knowledge base is the corpus of internal documents that a RAG system queries to ground its answers. This wording is a synthesis. It draws together the earlier definitions, which refer to a "document base" [1][28] and to the "company's own data" [4]. The company chooses the scope of this corpus, and that scope limits what the AI can use to answer.

An architecture that requires trained teams

The official guide, published in November 2024 on entreprises.gouv.fr, adds a point that the technical definitions leave aside. A RAG project requires efforts to familiarize teams with AI, which include training in prompt engineering, that is, in how to formulate requests to the system [2].

Several of the definitions cited here come from vendor websites [4][5][28][33]. They all converge on the principle: search the documents first, then answer.

How does a RAG differ from a general-purpose AI assistant?

A general-purpose AI assistant answers from what it learned during training, whereas a RAG grounds its answers in the documents the company has chosen. A sector provider summarizes it this way: the general-purpose model knows neither the organization's contracts, nor its procedures, nor its clients [18].

What the general-purpose assistant does not know

A general model relies on its training memory. What is specific to the company (a contract signed last month, an internal procedure, a client's history) is not part of that memory [18]. Without access to these documents, it produces at best a generic answer.

What RAG changes

RAG (Retrieval-Augmented Generation) makes generative AI answer from the company's documents rather than from its general knowledge [5]. Before writing, the system consults the documents, then uses them to answer [35]. In the case of customer service, the AI first searches sources defined by the company and uses that content as context. The answer thus stays closer to what already exists in the knowledge base, instead of depending solely on the model's "statistical memory" [34]. No model needs to be retrained for this [18].

A useful caveat

Connecting a model to documents does not, on its own, guarantee a good answer. Quality depends on how the documents are split and retrieved: poor chunking or imprecise retrieval degrade the result [31]. Several of the sources cited here come from vendors (software publishers, consultancies, agencies): their reliability claims should be verified on your own documents.

How does a RAG work, step by step?

A RAG chains a retrieval phase and a generation phase: the sources are indexed in a vector database, the relevant passages are retrieved, then passed to an LLM along with the question [17][31].

1. Indexing. The company's documents (intranet, internal reference materials, HR policies, quality guides [15]) are split into passages, then indexed in a vector database [17][31].
2. Retrieval. For each question, the system finds the most relevant passages in this database [17].
3. Generation. These passages are sent to the LLM with the question, in a prompt along the lines of: "Here is a user question and the relevant documentation excerpts. Answer only on the basis of these excerpts, and cite your sources." [31] The answer is thus produced with citations and traceability markers [14].

Two distinct mechanisms in one pattern

RAG is an architectural pattern that combines two distinct mechanisms [6]: a generative language model on the one hand, and a search engine connected to a knowledge base on the other [15]. It is therefore not a single model to choose, but an assembly whose each component is tuned separately.

Quality depends on every step

The quality of the result is determined at every step of the pipeline, not in the model alone. According to Kanbios, which describes a four-step process, poor chunking or imprecise retrieval degrade the result [31]. The prompt asking the model to answer only from the excerpts does not fix these flaws: if the passages provided are poorly split or off-topic, the model works from bad excerpts.

These checkpoints apply at each level:

• Chunking: a passage cut at the wrong place loses the context that made it useful [31].
• Retrieval: the search must surface the right excerpts, without missing any or bringing back irrelevant ones [31].
• Generation: the instruction to answer only from the excerpts and cite sources makes the answer verifiable [31][14].

For a CIO, this shifts the evaluation: first measure what the system retrieved, and only then what the model did with it.

What benefits can RAG deliver, and which should be qualified?

The benefits most often put forward for RAG are reliability, grounding of answers in a defined corpus, traceability and confidentiality. These promises come mainly from commercial players (providers, software publishers), and some should be taken with a grain of salt.

What is claimed

• Reliability: each answer relies on a base defined in advance (intranet, internal reference materials, HR policies, quality guides) rather than on the model's "statistical memory" [15].
• Traceability: the answer cites its source [12], and the system keeps citations and traceability markers [14]. One software publisher announces citations down to the exact passage (document, page, paragraph) [27]. The level of precision therefore depends on the offer.
• Confidentiality: one provider claims that internal documents become usable by AI "without compromising confidentiality" [20].
• Sovereignty: several players offer so-called "sovereign" deployments [9][21]. The file does not allow us to verify what this term covers from one offer to another.

What needs qualifying

The phrase "drastic reduction of hallucinations" comes from a commercial player, who writes that RAG reduces "drastically the errors and hallucinations" [16]. Other sources use a more measured verb: traceability serves to "limit" hallucinations [14]. The available excerpts do not quantify this reduction. It should therefore be read as an announced tendency, not as a measured guarantee.

The same caution applies to confidentiality and sovereignty: these are sales arguments, presented by those who sell them.

As for the architecture's limitations, one source announces that it addresses them [6], but the excerpts provided do not detail any. We therefore assert none here, and we do not conclude that they are absent either. Before adopting a benefit, it must be tested on your own documents, by checking the citations produced.

What are the use cases for AI on internal documents?

The documented uses of RAG share a common feature: the expected answer already exists in the organization's documents, and it must be found and then phrased. Four use cases emerge from the sources.

Searching a document base. According to France Num, generative AI, via RAG, can automate or assist tasks that require searching a document base, contracts being one example [1]. This is assistance with search, not a replacement for reading the documents.

Internal assistant. An internal AI assistant is fed with company documents, such as FAQs and product sheets. It applies the RAG principle: it searches this document base for relevant information before formulating its answer [24].

Customer service. With RAG, the AI first looks for information in sources defined by the company, then uses them as context to answer. The answer thus stays closer to what the knowledge base already contains, instead of depending solely on the model's statistical memory [34].

Typical document sources. The scope is set by the company. One provider cites the intranet, internal reference materials, HR policies and quality guides as sources [15]. Each answer then relies on a base defined in advance [15].

An example of a quick answer from a handbook

One provider describes the expected result this way: employees ask the chatbot a question and get the answer in 30 seconds, based on an up-to-date handbook [8]. This timeframe is a commercial promise, not an independent measurement. The example mainly illustrates the underlying condition: the answer is only useful if the source document is up to date.

These use cases do not require the same corpora. Contracts, product FAQs and HR policies have neither the same structure, nor the same access rights, nor the same update frequency, which directly affects how the sources must be prepared.

What determines the success of a RAG project?

The success of a RAG project depends less on the language model than on document quality, access rights and evaluation [13]. This finding comes from a commercial player, but the official guide and the other sources in this file also place the challenge in the documents and their uses rather than in the choice of model.

Preparing sources before connecting the AI

Source preparation is one of the steps to address explicitly in a RAG project [10]. The mechanism explains why: the retrieved passages are sent to the model with the question, in a prompt that asks it to answer only from these excerpts and cite its sources [31]. The whole challenge therefore lies in the quality of each of the four steps of the process. Poor chunking or imprecise retrieval degrade the result [31].

Defining access rights

Access rights are among the three success factors cited [13]. The sources in this file do not detail how they are implemented technically. It is up to the CIO to define, before deployment, who may query which documents.

Evaluating and verifying citations

Evaluation is the third factor identified [13], and verifying citations is a step in its own right [10]. A sourced answer is not for all that accurate: the link to the cited passage serves to check the answer, not to exempt it from review. The file does not describe a precise evaluation method.

Training teams on the tool and on prompt engineering

The guide on retrieval-augmented generation published by entreprises.gouv.fr in November 2024 recommends an effort to familiarize teams with AI [2]. This work has two components, including training in using the tool, notably in prompt engineering, that is, in how to formulate the requests sent to the RAG system [2].

How much does an enterprise RAG cost, and how do you get started?

According to one provider, an enterprise RAG costs between €10,000 and €50,000 to set up, then €1,000 to €3,000 per month to run [13]. These figures come from a commercial player, which ties them to French barometers [13]. They give an order of magnitude, not a reference price: the file does not contain these barometers, and the exact scope covered by these amounts is not detailed.

The same source states that success depends less on the model than on document quality, access rights and evaluation [13]. The budget should therefore focus on these workstreams, not only on technology.

Starting without a large budget

A first simple RAG can be started with a handful of documents, using tools such as NotebookLM or ChatGPT [11]. This step makes it possible to test the approach on a limited scope before committing to an investment of the order of magnitude cited above.

To make this first attempt instructive:

• Choose a limited, controlled corpus: a few documents whose content you know, so that an incorrect answer is easy to spot.
• Prepare the sources and verify citations: these are two methodological points emphasized in the specialized literature [10].
• Involve future users: the guide from the French Directorate General for Enterprises recommends an effort to familiarize teams, which includes training in the tool and in prompt engineering, that is, in formulating requests [2].

This test does not tell the whole story about scaling up. The points that, according to the commercial source, determine success (access rights, evaluation) [13] are not resolved by a simple trial on a few files. They must be examined before any broader deployment.

How do you build a knowledge base that AI can query with Oupi?

Oupi lets you build knowledge bases that AI can query. This matches the principle described above: the AI relies on a document corpus defined by the organization, rather than answering from memory.

The principle: a document base the AI consults before answering

In a RAG approach, a knowledge base is the set of documents the company considers relevant: FAQs, product sheets, intranet, internal reference materials, HR policies, quality guides [24][15]. The assistant first searches this document base for relevant information, then formulates its answer [24]. The answer thus stays closer to what already exists in the base, instead of depending only on the model's statistical memory [34].

This is the principle Oupi makes available: gathering documents into a knowledge base, then querying it with AI. The Chat with your documents and Enterprise pages of the site present the offer. This section does not detail its features or conditions.

What remains decisive, whatever the tool

Choosing a platform does not exempt you from the work described in the previous sections. The sources converge on a few points of vigilance:

• The content of the corpus: success depends less on the model than on document quality, access rights and evaluation [13]. One provider makes the same point: everything comes down to the documents [18].
• Source preparation: sources must be prepared, citations verified, and edge cases handled [10].
• Quality of the processing chain: poor chunking or imprecise retrieval degrade the result [31].
• Support for teams: the French government guide on RAG recommends an effort to familiarize teams, with training in the tool and in prompt engineering, that is, in how requests are formulated [2].

For an initial scope, the safest approach is to start from a restricted, controlled corpus. Some sources mention a first simple RAG built on a handful of documents [11]. Then check that the answers cite the expected passages before expanding the base.