{"id":833,"date":"2026-10-03T18:22:06","date_gmt":"2026-10-03T21:22:06","guid":{"rendered":"https:\/\/www.smartservices.com.ar\/?post_type=insight&p=833"},"modified":"2026-10-03T18:22:19","modified_gmt":"2026-10-03T21:22:19","slug":"what-is-rag","status":"publish","type":"insight","link":"https:\/\/www.smartservices.com.ar\/en\/insights\/what-is-rag\/","title":{"rendered":"What Is RAG? Retrieval-Augmented Generation Explained"},"content":{"rendered":"
\n

Short answer<\/h2>\n

Retrieval-augmented generation (RAG) combines information retrieval with text generation. When someone asks a question, the system finds potentially relevant passages, supplies them as context to a language model, and generates an answer. Merely retrieving a document does not train the model on it. This distinction lets teams update accessible evidence without necessarily changing model weights.<\/p>\n<\/section>\n

In one sentence<\/strong>Retrieval-augmented generation (RAG) retrieves relevant information and gives it to a generative model as context for an answer.<\/span><\/p>\n

\"Retrieval-augmented<\/a>
Concept diagram. Open for a larger view.<\/figcaption><\/figure>\n

RAG retrieves; it does not automatically train the model<\/h2>\n

The pattern is search \u2192 evidence \u2192 context \u2192 answer. RAG does not make a model know everything. It lets the model consult information before responding. Both retrieval quality and generation quality matter.<\/p>\n

Follow a question through the pipeline<\/h2>\n
    \n
  1. A user asks, \u201cWhat does our return policy require?\u201d<\/li>\n
  2. The application checks identity and access rights.<\/li>\n
  3. It searches approved documents, knowledge bases, or other sources.<\/li>\n
  4. It selects relevant passages and may rerank candidates.<\/li>\n
  5. It provides those passages to the model as context.<\/li>\n
  6. The model answers, ideally with usable source references.<\/li>\n<\/ol>\n

    None of these steps guarantees truth. A retrieved passage may be stale or incomplete. A fluent answer can cite a document that does not support its conclusion. Test retrieval, answer faithfulness, and the system’s ability to say \u201cI do not have enough evidence\u201d separately.<\/p>\n

    Prepare the sources, not just the model<\/h2>\n

    Many systems split documents into chunks<\/em> for retrieval. Chunks that are too small can lose an exception’s context; chunks that are too large can crowd out better evidence. Titles, versions, dates, language, and permissions help filtering. PDFs, tables, and appendices may need document intelligence<\/a> before they are useful.<\/p>\n

    Retrieval can use keywords, vector similarity based on embeddings<\/em>, or both. An embedding is a numeric representation used to compare content; it is not a readable copy or a guarantee of meaning. Reranking tries to improve the order of candidate passages. The right method depends on the corpus and the questions people actually ask.<\/p>\n

    Access control belongs inside retrieval<\/h2>\n

    Relevant content is not necessarily authorized content. Apply user permissions before a passage reaches the model, rather than hiding a citation after the answer is generated. Review shared indexes, caches, logs, and links back to original files. A public policy and a confidential contract can discuss the same subject; relevance and access are separate conditions.<\/p>\n

    RAG versus fine-tuning, search, and long context<\/h2>\n
    \n\n\n\n\n\n\n\n\n
    Different mechanisms, different jobs<\/caption>\n
    Approach<\/th>\nWhat it does<\/th>\nWhat it does not solve alone<\/th>\n<\/tr>\n<\/thead>\n
    RAG<\/td>\nRetrieves evidence for an answer.<\/td>\nPoor source quality or access controls.<\/td>\n<\/tr>\n
    Fine-tuning<\/td>\nAdjusts model behavior using examples.<\/td>\nReliable access to changing documents.<\/td>\n<\/tr>\n
    Search<\/td>\nReturns results for a person to inspect.<\/td>\nA bounded, synthesized answer.<\/td>\n<\/tr>\n
    Long context<\/td>\nAllows more material in a request.<\/td>\nSelecting authorized, current material.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

    These approaches can be combined. RAG does not require a vector database in every case. Keyword and hybrid retrieval can be stronger for exact terms. RAG is also not a transactional database. \u201cHow much stock is available now?\u201d may require a live API rather than a document indexed yesterday.<\/p>\n

    When it helps\u2014and when it does not<\/h2>\n

    RAG is useful for policies, manuals, technical documents, and other changing knowledge when sources have owners, versions, and testable answers. It adds little to one short deterministic rule. It cannot repair contradictory source documents or guarantee real-time operational values. Fixing the source or the integration<\/a> may matter more.<\/p>\n

    Frequently asked questions<\/h2>\n

    Does RAG train the model on my documents?<\/h3>\n

    No. It supplies retrieved passages as answer context. Training or fine-tuning is a separate operation.<\/p>\n

    Does RAG eliminate hallucinations?<\/h3>\n

    No. It can ground an answer, but retrieval can miss evidence and generation can still overstate what a source supports.<\/p>\n

    Do I need a vector database?<\/h3>\n

    Not always. Keyword search, filters, and hybrid retrieval may fit the corpus better.<\/p>\n

    Can RAG enforce user permissions?<\/h3>\n

    Yes, if authorized evidence is filtered for each request before it reaches the model. Hiding a result only in the interface is insufficient.<\/p>\n

    Does it work with PDFs?<\/h3>\n

    Yes, after usable content is extracted. Scans, tables, and mixed versions often need preparation.<\/p>\n

    How it fits a Smartservices solution<\/h2>\n

    When a workflow needs answers grounded in company information, Smartservices examines sources, permissions, retrieval, and review before choosing a model. RAG may support document intelligence<\/a>, private AI<\/a>, or agents<\/a>. It is a design pattern, not automatically a standalone product.<\/p>\n

    \n

    Sources and references<\/h2>\n
      \n
    1. Retrieval-Augmented Generation \u2014 Lewis et al.<\/a><\/li>\n
    2. RAG overview \u2014 Microsoft Learn<\/a><\/li>\n
    3. Vector search overview \u2014 Microsoft Learn<\/a><\/li>\n<\/ol>\n<\/section>\n