If you have ever asked an AI assistant a question based on a contract, a technical manual or an internal procedure and received something convincing but wrong, you have hit the main limitation of large language models (LLMs): they only know what they learned during training. The technique that solves this problem at its root is called RAG, short for Retrieval-Augmented Generation, and over the past two years it has become the reference architecture for bringing AI to the real documents of any organisation.
What is RAG: a plain-language definition
RAG is an architecture that combines two distinct capabilities: retrieving relevant fragments from a proprietary document base and generating a natural-language answer based on those fragments. The language model does not «remember» your procedures; it reads them at the moment they are needed, just like an adviser who consults the file before answering you.
The acronym was formalised by Meta AI researchers in 2020 (Lewis et al., «Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks»), but its mass enterprise adoption came from 2023 onwards, when large-scale LLMs became accessible via API and vector indices dropped in price to the point of being viable for any SME.
Why LLMs alone are not enough for internal documentation
A model such as GPT-4o, Gemini 1.5 Pro or Llama 3 has a knowledge cut-off date and, above all, does not know your company. It has not read your quality policy, your product catalogue, your supplier contracts or your security procedures. If you ask about them, it may generate a plausible but invented answer: the phenomenon known as hallucination.
Alternative approaches for injecting context have obvious limitations:
- Pasting the entire document into the prompt: works for small documents, but is expensive in tokens and fails with repositories of hundreds of files.
- Fine-tuning: retraining the model on your internal data. Useful for adapting style or vocabulary, but not for facts that change (prices, stock, regulations). The process is also costly and must be repeated every time content changes. More on when to use fine-tuning versus RAG.
- RAG: retrieves only the relevant fragments at query time. The marginal cost is low, content can be updated daily and the answer is always anchored in a real, citable source.
How RAG works, step by step
The flow has two clearly distinct phases: indexing (which happens once, when the system is prepared) and inference (which happens every time a user asks a question).
Phase 1: document indexing
- Document ingestion: files are loaded in common formats (PDF, Word, Excel, email, internal web pages, support tickets…).
- Chunking: each document is split into manageable fragments, usually between 300 and 800 tokens, respecting paragraphs or sections.
- Embedding generation: each fragment is converted into a numerical vector by an embedding model (for example,
text-embedding-3-largefrom OpenAI orembed-v3from Cohere). That vector captures the semantic meaning of the text. - Storage in a vector index: the vectors are stored in a specialised database (Pinecone, Weaviate, Qdrant, pgvector on PostgreSQL…) that allows similarity searches in milliseconds.
Phase 2: inference (answering a query)
- Query vectorisation: the user's question is also converted into a vector using the same embedding model.
- Retrieval: the index returns the K fragments most semantically similar to the question (typically K = 3 to 10).
- Prompt construction: the retrieved fragments are inserted into the LLM's context alongside the original question.
- Generation: the LLM produces a natural-language answer, citing or drawing on the fragments. If the information is not in the fragments, it can indicate that it does not have sufficient data, eliminating hallucination.
Comparison: RAG versus other personalisation strategies
| Criterion | Long prompt (full context) | Fine-tuning | RAG |
|---|---|---|---|
| Cost per query | High (many tokens) | Low after training | Medium-low |
| Content update | Immediate | Requires retraining | Immediate (reindex) |
| Source traceability | Manual | None | Automatic (cites the fragment) |
| Document scale | Limited by context window | Unlimited (model memorises it) | Unlimited (retrieves only what is relevant) |
| Hallucination risk | Low if doc fits in context | Medium-high (poorly memorises facts) | Low (anchored in real sources) |
| Data privacy | Data travels to provider on each call | Data travels during training | Configurable (on-premise or private cloud) |
| Implementation complexity | Very low | High | Medium |
Real use cases in Spanish SMEs
RAG is not a laboratory technology. In 2025 and 2026 it is in production at organisations of very different sizes. These are the most common patterns:
Internal documentation assistant
Employees ask in natural language about procedures, company policies, product manuals or internal regulations and receive answers with an exact reference to the source document. This drastically reduces search time and errors caused by consulting outdated versions.
First-level technical support
Industrial firms, manufacturers and distributors connect their knowledge base of incidents, technical data sheets and service manuals to a chatbot that answers field technicians or customers directly. The rate of escalation to human support falls by 30% to 60% according to cases documented by software vendors such as Zendesk and Intercom in their 2025 reports.
Legal and compliance assistant
Law firms, accounting offices and compliance departments index contracts, collective agreements, applicable legislation and case law. The assistant answers specific questions («What does the penalty clause in the contract with supplier X say?») and cites the exact paragraph.
Copilot over ERP and structured data
Combined with connectors that extract real-time data from the ERP, RAG allows questions such as «How many units of item 4412 are left in the Burgos warehouse?» to be answered without the user having to open the system. Our RAG implementation for SMEs covers precisely this integration with Odoo, Sage and Dynamics.
Advanced RAG architectures: beyond the basic case
The simplest RAG implementation — chunk, vectorise, retrieve, generate — works well in many cases, but has limitations. The industry has developed variants for more demanding scenarios:
- Hybrid RAG: combines vector (semantic) search with classic lexical search (BM25). Improves accuracy when documents contain very specific terms or internal codes that embeddings do not capture well.
- Reranking: after retrieving K candidates, a second model (cross-encoder) reorders the fragments by actual relevance before sending them to the LLM. Reduces noise in the context and improves response quality.
- Knowledge-graph RAG: complements the vector index with structured relationships between entities (products, customers, suppliers). Enables relational questions that vector search alone cannot resolve.
- Agentic RAG: the LLM autonomously decides when to retrieve information, from which source and with which query. Suitable for multi-step reasoning tasks where the necessary information is not obvious upfront.
Privacy and data sovereignty considerations
The question most IT managers and DPOs raise when evaluating RAG is: where does the data go? The answer depends on how the system is deployed:
- LLM provider cloud: the retrieved fragments travel in the context of each API call. The main providers (OpenAI, Google, Mistral AI) offer GDPR data processor agreements and zero-data-retention options for enterprise customers. This is the fastest option to deploy.
- On-premise deployment: both the vector index and the language model run within the corporate perimeter. No data leaves. This option, which we cover in detail in our sovereign AI service, is appropriate for regulated sectors (healthcare, defence, banking) or when documents contain sensitive information that cannot be relinquished.
- Private cloud or dedicated VPC: the common middle ground: the LLM runs in a cloud environment managed by the customer (Azure OpenAI Service, AWS Bedrock, Google Vertex AI) where data is not used for training and residency can be fixed in the EU.
From a GDPR perspective, document fragments sent to the LLM with each query constitute data processing. It is necessary to document the legal basis, review whether the documents contain personal data and, where applicable, pseudonymise or anonymise before indexing.
Factors that determine the success of a RAG implementation
In our experience accompanying SMEs since 2007, RAG projects that fail do not fail because of the technology. They fail for these reasons:
- Poor document quality: scanned documents without OCR, PDFs with tables as images, files without structure. The vector index is only as good as the documents it ingests.
- Poorly calibrated chunking: fragments that are too short lose context; too long and they dilute the semantic signal. The optimal size depends on the document type.
- Absence of metadata: without metadata (author, date, department, document type), the system cannot filter by context and retrieves irrelevant or outdated fragments.
- Incorrect expectations: RAG does not turn documentary chaos into order. If procedures are contradictory or outdated, the assistant will reflect that.
- Lack of an improvement cycle: RAG systems improve with the logging of unanswered questions and periodic evaluation of the relevance of retrieved fragments.
Frequently asked questions
Are RAG and semantic search the same thing?
Not exactly. Semantic search is the retrieval component of RAG: it finds relevant fragments by meaning, not by exact keyword match. RAG adds the generation step: it uses those fragments as context for an LLM to produce a natural-language answer. You can have semantic search without RAG (the result is a list of fragments), but RAG always includes semantic or hybrid search.
How long does it take to have a working RAG over my company's documents?
A pilot on a bounded corpus (for example, 200 documents from one department) can be up and running in two to four weeks, including document preparation, index configuration and quality testing. A corporate deployment with ERP integration, role-based permission management and continuous monitoring requires between two and five months. The bottleneck is usually document cleaning and structuring, not the technology.
Does RAG guarantee that AI will never make up information?
It drastically reduces the risk, but does not eliminate it entirely. If the question has no coverage in the corpus, the LLM may generate a generic answer. That is why well-designed systems include a similarity threshold: if no fragment exceeds that threshold, the assistant replies that it does not have enough information rather than inventing. That behaviour requires explicit configuration and testing.
Is an IT department needed to maintain a RAG system?
Not necessarily. Cloud-managed RAG systems (Azure AI Search + Azure OpenAI, for example) allow the index to be updated through administration interfaces without developer involvement. For highly dynamic corpora (documents that change daily), automatic reindexing pipelines are configured. The IT capacity required is proportional to the integration complexity, not to the RAG technology itself.