{"id":5404,"date":"2025-09-10T09:11:22","date_gmt":"2025-09-10T09:11:22","guid":{"rendered":"https:\/\/dataguru.cloud\/?p=5404"},"modified":"2025-09-10T09:11:27","modified_gmt":"2025-09-10T09:11:27","slug":"los-3-pasos-para-construir-un-rag-eficiente","status":"publish","type":"post","link":"https:\/\/dataguru.cloud\/en\/los-3-pasos-para-construir-un-rag-eficiente\/","title":{"rendered":"The 3 Steps to Building an Efficient RAG System"},"content":{"rendered":"<p>In the dynamic business landscape, Retrieval Augmented Generation (RAG) systems are emerging as a pragmatic and secure solution for implementing artificial intelligence. Unlike traditional models, RAG guarantees improved answer accuracy by reducing \"hallucinations\" and ensures access to up-to-date information by querying real-time databases. This technology allows for the secure use of private or proprietary data, which is crucial for confidentiality, and promotes transparency and auditability by citing information sources.<\/p>\n\n\n\n<p>Beyond its operational advantages, RAG is remarkably cost-effective compared to model retraining. Instead of investing large sums in updating models, RAG adapts AI to new data efficiently and affordably, democratizing its use and turning it into a tangible and manageable competitive advantage.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 1: Data Preparation - The Pre-process for an Optimal RAG<\/strong><\/h3>\n\n\n\n<p>The effectiveness of a RAG system depends directly on the quality and format of the data it's fed. Before implementation, it's crucial to pre-process the data, ensuring the information is in the most suitable format for optimal retrieval. This step is fundamental for the RAG to provide accurate and relevant answers.<\/p>\n\n\n\n<p>La elecci\u00f3n del formato de datos es clave y var\u00eda seg\u00fan el tipo de informaci\u00f3n:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Unstructured Data:<\/strong> For documents like corporate policies, user manuals, or product technical documentation, where free-form text is the main element, an unstructured format like a PDF or text document is ideal.<\/li>\n\n\n\n<li><strong>Semi-structured Data:<\/strong> In cases where the structure isn't rigid and the number of elements can vary, such as a company's hierarchical structure with a changing number of subsidiaries and sub-subsidiaries, the JSON format is most suitable.<\/li>\n\n\n\n<li><strong>Structured Data:<\/strong> If the information is organized in rows and columns, like sales records or customer databases, the SQL format is optimal, as it allows for efficient queries and filtering.<\/li>\n\n\n\n<li><strong>Semantic Search Data:<\/strong> For searches based on text, image, audio, or video similarity, the best option is a vector database. These databases store information as numerical vectors, which facilitates the identification of conceptual similarities beyond simple keyword matching.<\/li>\n\n\n\n<li><strong>Graph Data:<\/strong> When information focuses on the relationships between elements (e.g., \"my friends' friends\"), a graph database is the ideal format because its structure is optimized for efficiently navigating and analyzing these connections.<\/li>\n<\/ul>\n\n\n\n<p>There are many additional cases that must be analyzed thoroughly before implementing a RAG system. The correct preparation and formatting of information are the foundation for maximizing the performance and utility of any such AI solution.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 2: Data Access - The Balance Between Accuracy, Latency, and Cost<\/strong><\/h3>\n\n\n\n<p>An efficient information access system, like the one used in a RAG, is based on balancing three crucial factors: the accuracy of results, the latency in obtaining them, and the associated cost. Accuracy refers to the relevance and correctness of the information, while latency measures the response speed. Cost encompasses the computational resources needed for data processing. It's not about maximizing one of these elements in isolation, as there's often a trade-off among them. An effective system must find the right balance for each specific use case; for example, an application requiring speed might sacrifice a bit of accuracy, or vice versa.<\/p>\n\n\n\n<p>Next, we'll explore the various data access techniques that help achieve this balance.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Unstructured Data<\/strong><\/li>\n<\/ul>\n\n\n\n<p>For searching large volumes of documentary information, such as manuals, articles, or reports, different techniques exist based on data volume, latency, and required accuracy.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Low Data Volume (&lt; 10TB):<\/strong> For smaller datasets, managed vector search solutions like Google Vertex AI Vector Search are extremely efficient. They offer high accuracy and low latency at a reasonable cost, making them an ideal starting option.<\/li>\n\n\n\n<li><strong>High Data Volume (&gt; 10TB):<\/strong> As the volume of information grows, managed solutions become prohibitively expensive. In these cases, vector search strategies that require data pre-processing\u2014known as <em>chunking<\/em> and <em>embedding<\/em>generation\u2014are used. During <em>chunking<\/em>the document is divided into smaller text fragments. Here, there is a critical balance between accuracy and latency: a smaller <em>chunk<\/em> size increases accuracy (the model can focus on specific details) but also increases latency and computational costs due to the larger number of fragments to process.<\/li>\n<\/ul>\n\n\n\n<p>However, a limitation of pure semantic search is the risk that the system might miss keywords with low semantic weight, which can lead to model \"hallucinations.\" For example, when searching for the technical specifications of \"part XJ-25,\" the system might mistakenly suggest those of \"part XJ-26\" if the term \"technical specifications\" appears more frequently in that document, making its vector semantically closer to the initial prompt.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Semi-structured and Graph Data<\/strong><\/li>\n<\/ul>\n\n\n\n<p>In this case, flexibility is key. A combination of information in its original format (text, numerical, date, etc.) with its vectorized representations allows for a high-accuracy, low-latency hybrid search. This strategy combines literal search(based on keywords or ranges) with vector search (based on semantic meaning), which increases cost reasonably by optimizing the retrieval of complex data. This approach is especially useful in dynamic structures, such as a company's hierarchy, where combining both methods maximizes RAG effectiveness.<\/p>\n\n\n\n<p>Similarly, in graph databases, where information is based on relationships between elements (e.g., \"my friends' friends\"), combining the graph's structure with the LLM's ability to understand those connections allows for relationship searches with high accuracy, low latency, and very controllable costs. The flexibility and structure of graphs perfectly complement LLM processing, which facilitates the retrieval of relational information.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Structured Data<\/strong><\/li>\n<\/ul>\n\n\n\n<p>Handling data in tabular formats like SQL databases requires a different approach.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Pre-defined SQL:<\/strong> The simplest strategy is to use pre-defined SQL queries. In this method, the LLM extracts filters (e.g., WHERE, HAVING clauses) from the user's <em>prompt<\/em> to apply them to an existing SQL statement, which is then launched directly to the database. This approach is highly accurate and efficient, as the model doesn't have to \"invent\" the query.<\/li>\n\n\n\n<li><strong>Ad-hoc SQL Generation:<\/strong> If the LLM is required to generate the SQL query from scratch, the process becomes much more complex. The model needs an exhaustive description of the data schema: table content, field meanings, aggregation levels, and relationships between tables. As the data schema grows in complexity, this process requires multiple refinement steps to ensure the generated SQL is syntactically correct and semantically accurate. This approach has a high risk of error and, although powerful, is significantly more difficult to implement than a SQL template-based solution.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 3: Advanced Data Access - Orchestrating Multiple Steps<\/strong><\/h3>\n\n\n\n<p>To achieve maximum accuracy and efficiency in complex RAG systems, simple direct search isn't always enough. Advanced information access techniques, based on orchestrating multiple steps, combine different search methods to optimize data retrieval, achieving an ideal balance between accuracy, latency, and cost. Here are the strategies for different data types.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Unstructured Data: Combining Keyword and Semantic Search<\/strong><\/li>\n<\/ul>\n\n\n\n<p>In accessing large volumes of unstructured data, a pure vector search can lead to inaccuracies, especially if keywords have low semantic weight in the overall embedding. The optimal solution is a multi-stage hybrid search strategy:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Initial BM25 Indexing and Search:<\/strong> The first step is to use a keyword search algorithm like BM25 (<em>Best Matching 25<\/em>). This technique indexes documents based on the frequency and rarity of words. Upon receiving the query, BM25 quickly identifies and retrieves an initial set of documents containing the exact keywords. This ensures high lexical accuracy, preventing the system from omitting crucial terms that a purely semantic search might ignore.<\/li>\n\n\n\n<li><strong>Semantic KNN Search:<\/strong> Once the universe of documents is reduced, semantic search is applied. Using a vector database, a KNN (<em>k-Nearest Neighbors<\/em>) algorithm is used to find the <em>k<\/em> text chunks that are semantically closest to the initial query within the pre-selected document set. This stage refines the search by capturing the contextual meaning of the query, even if the exact words don't match. For example, if a user asks about \"software problems,\" semantic search might find documents that discuss \"programming bugs\" or \"system errors.\"<\/li>\n\n\n\n<li><strong>Re-ranking and Verification:<\/strong> The results from the previous two steps are combined and submitted to a re-ranking phase. A smaller, specialized language model can be used to evaluate the relevance of each result relative to the original <em>prompt<\/em> . Finally, a final verification ensures that the LLM's generated response is based only on the content of the most relevant retrieved documents. This process minimizes hallucinations and guarantees coherence with the source data.<\/li>\n<\/ol>\n\n\n\n<p><em>For example,<\/em> A user asks: \"Give me the technical specifications of router model XJ-25 and how to fix a connection failure.\"<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Step 1 (BM25):<\/strong> The system searches for documents containing \"XJ-25,\" \"router,\" \"technical specifications,\" and \"connection failure.\"<\/li>\n\n\n\n<li><strong>Step 2 (KNN):<\/strong> It takes the BM25 results and searches for chunks semantically closest to the <em>prompt<\/em>. The system might find a technical manual on the model's specs and a support forum discussing \"firmware reset\" as a solution to connectivity issues.<\/li>\n\n\n\n<li><strong>Step 3 (Re-ranking):<\/strong> Less relevant documents are discarded, and only those dealing with the XJ-25 model and its specific problems are used. The LLM generates the response by combining information from the manuals and forums, verifying that the data aligns with the retrieved sources.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Hybrid and Graph Data: Orchestration for Complex Searches<\/strong><\/li>\n<\/ul>\n\n\n\n<p>Orchestration becomes even more critical when data isn't limited to a single type.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Hybrid Searches (Text and Vector):<\/strong> In databases that store diverse information\u2014such as text, dates, or numbers\u2014along with vector data, hybrid search is the most effective solution. The RAG system can launch queries that combine literal search (e.g., searching for sales records within a specific date range, like (sales &gt; 1000 AND date &gt; '2025-01-01')) with vector search (e.g., searching for products that are semantically similar to \"travel accessories\"). This allows for high-accuracy, low-latency queries, integrating the efficiency of relational databases with the power of semantic search.<\/li>\n\n\n\n<li><strong>Graph Searches:<\/strong> In graphs, information is based on relationships. Before an intensive search, a relational database can be used to limit the search universe. <em>For example,<\/em> if we want to find \"my friends' friends who live in Barcelona,\" the query could be orchestrated in two steps:\n<ol class=\"wp-block-list\">\n<li><strong>Step 1 (Relational Database):<\/strong> A user table is queried to get a list of the current user's friends who reside in \"Barcelona.\" This is a quick, literal search.<\/li>\n\n\n\n<li><strong>Step 2 (Graph Database):<\/strong> With that list of friends, a specific query is launched to the graph database to find the \"friends of those friends.\" This approach avoids a full graph traversal, which would be much more costly and slow, by limiting the search space to a relevant subset. This type of orchestration minimizes cost and latency while maximizing accuracy in retrieving relational information.<\/li>\n<\/ol>\n<\/li>\n<\/ul>\n\n\n\n<p>In conclusion, designing an effective and high-performance RAG system goes beyond merely choosing a data access technique. From optimizing data pre-processing based on its structure to using vector and hybrid search techniques and advanced multi-step orchestration, every decision impacts the system's effectiveness. The final implementation of a RAG is an art that combines science and engineering, requiring <strong>fine-tuning<\/strong> through trial and error to find the perfect combination that offers the <strong>lowest latency<\/strong>, the <strong>highest accuracy<\/strong> , and <strong>reasonable costs<\/strong> that each specific use case demands. It is in this process of experimentation and optimization that the true potential of Retrieval Augmented Generation as a pillar of enterprise AI is discovered.<\/p>","protected":false},"excerpt":{"rendered":"<p>En el din\u00e1mico entorno empresarial, los sistemas RAG (Retrieval Augmented Generation) emergen como una soluci\u00f3n pragm\u00e1tica y segura para la [&hellip;]<\/p>\n","protected":false},"author":6,"featured_media":5406,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[],"tags":[27,32,33,31],"class_list":["post-5404","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","tag-ai","tag-data","tag-llm","tag-rag"],"_links":{"self":[{"href":"https:\/\/dataguru.cloud\/en\/wp-json\/wp\/v2\/posts\/5404","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dataguru.cloud\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dataguru.cloud\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dataguru.cloud\/en\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/dataguru.cloud\/en\/wp-json\/wp\/v2\/comments?post=5404"}],"version-history":[{"count":1,"href":"https:\/\/dataguru.cloud\/en\/wp-json\/wp\/v2\/posts\/5404\/revisions"}],"predecessor-version":[{"id":5407,"href":"https:\/\/dataguru.cloud\/en\/wp-json\/wp\/v2\/posts\/5404\/revisions\/5407"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dataguru.cloud\/en\/wp-json\/wp\/v2\/media\/5406"}],"wp:attachment":[{"href":"https:\/\/dataguru.cloud\/en\/wp-json\/wp\/v2\/media?parent=5404"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dataguru.cloud\/en\/wp-json\/wp\/v2\/categories?post=5404"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dataguru.cloud\/en\/wp-json\/wp\/v2\/tags?post=5404"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}