How to increase accuracy and reliability
Executive Summary
Artificial intelligence agents for corporate knowledge management tend to lose effectiveness as they grow.
In a simple agent, with a documentary base of thirty documents, accuracy can exceed ninety percent; however, when expanded to five hundred documents, that accuracy can fall to levels of sixty-five percent or less. When the documentary base reaches two thousand documents, the accuracy is so low that the agent is no longer useful.
However, this degradation is not inevitable: companies that implement custom RAG architectures—which intertwine hybrid vector and lexical search, reorder results with specialized models, incorporate knowledge graphs and use genetic search—maintain an accuracy that exceeds ninety percent, no matter how extensive their documentary bases are.
This study analyzes the specific technologies that make the difference between mediocre platforms and highly reliable business solutions, exposing why standard implementations are subject to structural precision limits and how cutting-edge architectures overcome these barriers through a refined integration of multiple complementary technologies.
When such integration is orchestrated with the appropriate configuration, knowledge management ceases to be a simple operational friction point and becomes a competitive advantage.
1. The gap between promise and reality in enterprise AI
1.1 The gap between promise and reality in enterprise AI
A pharmaceutical logistics firm based in Madrid invested quite a considerable amount of resources into implementing an artificial intelligence system that, according to its own expectations, would radically reconfigure the way it accessed operational documentation accumulated over more than fifteen years: security protocols, customs procedures, regulatory requirements and technical specifications for the handling of sensitive products.
During the testing period, the system showed reasonably acceptable performance, achieving the levels of precision that the supplier described as sufficient for production; with these results, the chief operating officer approved the deployment throughout the organization, anticipating significant improvements in efficiency and a marked reduction in operational errors.
Three months after the deployment, a warehouse employee re-questioned the system about the exact temperature requirements for storing seasonal flu vaccines, a question that is repeated dozens of times during the immunization campaign.
With the authority conferred on it by its response history, the system stated that the product could be kept at room temperature, when in reality the vaccine requires refrigeration between two and eight degrees Celsius, accidentally mixing information from other drugs with diametrically different storage requirements.
For several weeks, the failure was completely under the radar; the answer seemed reasonable and the system displayed it with the same confidence it gives to correct data.
It wasn't until a routine health inspection discovered the non-compliance that the repercussions were revealed, which not only entailed fines and regulatory sanctions, but also hit the company's reputation and undermined internal trust in technological tools.
1.2 Why is this pattern shown as the norm and not as an exception?
The company didn't neglect or get carried away by low-cost options; instead, it adopted what the vendor advertised as a “complete enterprise artificial intelligence solution”, relying on standard RAG technology that includes semantic vector search, automatic document fragmentation and a state-of-the-art language model.
The real obstacle was not the isolated quality of each of these components, but rather the structural limitations of simplified architectures that, although sufficient for small documentary bases, lack the necessary sophistication to preserve precision when the volume and complexity of information becomes more intricate.
1.3. Empirical Evidence: Examining the Deterioration of Accuracy When Scaling
At the same time, GigaSpaces researchers, when analyzing RAG agents that handle documentation repositories larger than one hundred megabytes, discovered that sets of poor quality or poorly structured documents produced erroneous answers in up to 40% of queries; even well-maintained repositories showed error rates ranging from 15% to 25% when questions required complex reasoning or the synthesis of distributed information (GigaSpaces, 2024).
The more extensive your documentary base, the more sophisticated the architecture must be to maintain levels of reliability that justify organizational trust in the agent.
1.4. The problem of semantic noise in large documentary bases
The origin of the challenge lies both in the mathematical basis and in the structure of the system itself: elementary RAGs translate each document into a high-dimensional vector representation—a kind of numerical fingerprint that captures its semantic sense—and, when the user formulates a query, the engine searches among the documents those whose vector fingerprints are closest to the fingerprint of the question.
With a small number of documents, this search for similarity is still reasonably effective, since the thematic differences are very marked: the vector footprint of a chemical safety manual is clearly different from that of a customs procedure.
In the scenario where the database brings together thousands of documents and, almost inevitably, many of them share the same technical repertoire — words such as “requirements”, “regulations”, “storage”, “temperature”, “procedure” — vector fingerprints begin to intertwine like threads of a skein, generating a tangle that baffles the motors of similarity and produces what the academic community has called “semantic noise”. Such interference, far from being an isolated incident, systematically corrodes the accuracy with which documents are retrieved (Toloka Research, 2024).
2. Limitations of simple RAG architectures
2.1. Exclusive dependency on semantic vector search
To understand why generic solutions run up against an inherent precision ceiling, regardless of the power of the underlying language models, it is necessary to analyze the structural limitations that accompany the most basic implementations. These restrictions, practically invisible to end users who only observe the response to their queries, are what ultimately determine the quality and reliability of the agent as a whole.
The first critical limitation is to rely exclusively on semantic vector search to extract relevant documents. Although this strategy represents a significant leap from traditional keyword-based searches—capturing conceptual similarity and allowing a query about “remote work” to retrieve documents that use expressions such as “telecommuting” or “working from home” —it suffers from notorious deficiencies with precise technical terminology, internal organization codes and specialized acronyms that rarely appear in the training corpus of the embeddings model.
2.2. Mechanical fragmentation: dismantling the context in order to achieve simplicity
The second structural limitation lies in the document fragmentation strategies that most basic implementations employ: a mechanical division of documents into fixed-length fragments, typically every five hundred or a thousand words, without any consideration of the semantic or logical structure of the content.
This approach, although computationally simple and quick to implement, systematically destroys critical context by arbitrarily cutting into ideas, multi-step procedures, enumerated lists of requirements, or technical specifications that require several consecutive paragraphs to understand.
2.3. The evidence that contrasts semantic fragmentation with mechanical fragmentation
The second structural limitation lies in the document fragmentation strategies that most basic implementations employ: a mechanical division of documents into fixed-length fragments, typically every five hundred or a thousand words, without any consideration of the semantic or logical structure of the content. This approach, although computationally simple and quick to implement, systematically destroys the critical context by arbitrarily cutting. IBM Research research on fragmentation strategies shows that conscious semantic fragmentation—which identifies natural thematic boundaries and respects the integrity of complete ideas—surpasses mechanical fragmentation by margins of thirty to fifty percent in retrieval quality (IBM Research, 2024).
However, most commercial implementations continue to employ mechanical fragmentation because their simplicity reduces implementation time at the expense of long-term accuracy.
2.4. Surface recovery: When mere mathematical similarity doesn't work
The third essential limitation, which we could call superficial recovery, lies in the fact that the agent carries out a single vector search, classifies the candidates by their mathematical similarity and gives the language model the ten or twenty best-placed fragments, based on the assumption that this superficial similarity in vector space amounts to true relevance for the specific query.
It's problematic to assume that vector similarity is sufficient; that metric captures general semantic relationships, but fails to unravel the specific nuances that the user actually needs. Thus, a document may seem semantically related because it addresses the correct broad topic, and yet it completely lacks the specific information that the query seeks.
2.5. The quantifiable impact of a sophisticated rearrangement
Studies of real implementations show that agents that incorporate a sophisticated reordering — a second phase in which specialized models analyze each candidate in depth, taking into account the specific query — increase the final quality of the answers between 25% and 40% (Toloka Research, 2024).
2.6. Absence of relational reasoning: Treating documents as islands
The fourth structural limitation, often underestimated but essential, is the lack of relational reasoning capacity: elementary RAG agents process each fragment as an isolated piece, without capturing the structural interconnections between documents—which one replaces which, which policy applies to which department, which procedure needs what prerequisites—leaving them without resources to answer questions that require going through those relationships or combining information dispersed in multiple conceptually linked documents.
2.7. GraphRag for reasoning about relationships
The most recent Microsoft Research studies on GraphRAG highlight that architectures that integrate knowledge graphs—and therefore capture structural relationships with greater fidely—far surpass traditional vector RAG, especially in complex queries that occur daily in real business environments (Microsoft Research, 2024).
3. Technological Innovations in RAG Architectures
3.1. Overview: The contrast between sophisticated integration and isolated components
The transition from basic RAG agents to highly reliable enterprise architectures is not just about adding more computing power or using larger language models; the essential thing is to carefully integrate various complementary technologies.
When these pieces are properly configured, according to the particularities of the documentary base and the organization's use cases, the structural limitations of simplified approaches are overcome.
3.2. Hybrid search: integrating vector and lexical techniques
Hybrid search, which merges semantic vector retrieval with traditional lexical search based on the frequency of terms, constitutes the first major architectural leap.
While vector search captures conceptual relationships and semantic similarity on a large scale, lexical search—usually implemented using algorithms such as BM25, which weigh exact matches and the frequency of terms in documents—provides complementary precision, especially useful for specific technical terminology and proper names.
3.3. Fusion Algorithms: Reciprocal Rank Fusion and Alternatives
Combining both approaches using fusion algorithms such as Reciprocal Rank Fusion —which integrate the rankings of the two systems, taking advantage of their strengths and compensating for their weaknesses—there are improvements of between 35% and 45% in retrieval accuracy compared to the exclusive use of vector search (Databricks Research, 2024).
3.4. Reordering using specialized models: cross-encoders
Reordering based on specialized models—often called cross-encoders in the technical bibliography—is the second innovation that differentiates business agents from the most basic implementations.
After the initial, agile and superficial search, which yields between twenty and thirty candidates using only rudimentary mathematical similarity, these reordering models examine each option in depth.
They are not limited to vector embeddings; they analyze the entire text and assess specific relevance by cross-referencing the content of the fragment with the user's exact query.
3.5. Quantifying the effect of reranking on the quality of responses
This evaluation, although it requires much more computation, is significantly more accurate: it allows us to distinguish between candidates who at first glance seem similar and those that actually contain the information sought by the query, which translates into documented improvements of between 30% and 50% in the final quality of the answers generated (Cohere Research, 2024).
The quality of the reordering model is, therefore, crucial; generic models perform reasonably, but specialized models—trained in specific domains such as legal, medical or technical—consistently outperform their generic counterparts.
3.6. GraphRag: Architecture based on knowledge graphs
GraphRag, the architecture conceived by Microsoft Research that combines knowledge graphs with traditional vector retrieval, constitutes a paradigmatic milestone for those queries that require reasoning about the network of relationships between entities or the synthesis of distributed information.
In contrast to the conventional vector RAG, which treats each fragment in isolation, GraphRAG generates structured representations in which the nodes represent entities—documents, sections, concepts, people, departments—and the links encode explicit relationships, such as: this document replaces that one, this policy applies to that department, this procedure requires that prerequisite.
3.7. Navigating structural relationships with GraphRag
The structural representation of the graph gives the agent the ability to explore conceptual relationships in a way that a flat vector search cannot do; thus, it can answer questions such as “what corporate policies specifically affect temporary employees in the Barcelona office?” navigating the graph instead of relying solely on vector similarity (Microsoft Research, 2024).
The researchers note that GraphRAG surpasses traditional RAG, especially in questions that require understanding structural relationships or multi-step reasoning, where the answer depends on linking information from different related sources.
3.8. Augmented Generation with Cache (CAG): Latency and its limitation
Cached Augmented Generation, or CAG, optimizes a critical bottleneck that basic implementations often ignore: the latency and computational cost of regenerating the entire context for each query.
The CAG caches fragments of frequently accessed documents and partially processed contexts. This way, subsequent queries may reuse a previously processed context instead of completely regenerating it. This reduces response latency significantly without sacrificing accuracy.
3.9. Multi‑strategy genetic search: From passive retrieval to active exploration
Perhaps multi‑strategy genetic search is the most sophisticated evolution of RAG architectures, since it replaces the old passive and unique recovery scheme with active and adaptive exploration.
Instead of launching a static vector search and merely displaying the results, agentic systems resort to more elaborate strategies: a multi-jump search—the agent reformulates the question and repeats the search each time the information retrieved is insufficient.
Then comes progressive adaptive filtering that starts with very restrictive criteria and gradually loosens them if the situation requires it, and cross-validation that explores independent sources for confirmation and explicitly warns when it detects contradictions, instead of arbitrarily deciding between conflicting versions.
3.10. Tailor-made embeddings for specific domains
The choice of an embedding model tailored to the specific domain of corporate documentation is an architectural decision that often escapes basic implementations, although its impact on final accuracy is significant. While generic embeddings, such as OpenAI's text-embedding‑ada, work reasonably well with general-purpose texts, specialized models—trained on corpora from specific sectors such as legal, medical, financial, or technology—capture nuances of terminology and conceptual relationships that generic approaches simply don't perceive.
3.11. Enhancing recall through specialized embeddings
When corporate documentation makes use of a specialized technical lexicon, industry acronyms and legal terminology of surgical precision, betting on generic embeddings is equivalent to leaving unexploited precision that could well have been exploited.
In contrast, tailor-made models can boost recall — the ability to locate each relevant document that resides in the database — between twenty-five and forty percent (Weaviate Research, 2024)
4. Integrated architectures, the art of fine-tuning configurations
4.1. Beyond the mere sum of the individual components
The gap between agents that achieve seventy-five percent accuracy and those that reach ninety-two is rarely reduced to a single superior technological component; rather, it stems from a sophisticated integration and an expert configuration of multiple technologies that operate in a coordinated manner within a cohesive architecture, where each layer complements and reinforces each other.
4.2. Intelligent preprocessing path
A solid business architecture links, step by step, an intelligent preprocessing pipeline that extracts documents without altering their original format—it preserves the tables as tables, maintains the numbering of the lists and, through advanced OCR, processes images with text—thus avoiding the conversion to plain text that usually destroys critical structural information.
This pipeline also normalizes formats while preserving essential metadata and automatically generates rich metadata — thematic category, date of creation and update, version, author, approval level — which then allows for filters and sophisticated prioritization during the search.
4.3. Search layer with multiple strategies
The multi‑strategy search layer works in parallel, launching a hybrid vector‑lexical search; at the same time, it distributes the document set by domain through intelligent partitioning, which significantly reduces the effective search space.
The results are then filtered based on metadata, taking into account both the nature of the query and the configured agent preferences.
The process ends with the generation of a batch of candidates that seeks to balance the recall — to ensure that truly relevant documents are present — with precision — and to minimize the inclusion of marginally relevant documents that generate useless noise.
4.4. Layer dedicated to deep reordering
A solid business architecture links, step by step, an intelligent preprocessing pipeline that extracts documents without altering their original format—it preserves the tables as tables, maintains the numbering of the lists and, through advanced OCR, processes images with text—thus avoiding the conversion to plain text that usually destroys critical structural information. This pipeline also normalizes formats while preserving essential metadata and automatically generates rich metadata—Category Upon reaching the reordering layer, the set of candidates is thoroughly examined by specialized models, which reorganize it according to the real relevance given to it by the specific thematic query, date of creation and update, version, author, level of approval—which then allow filters and sophisticated prioritization during the search.
4.5. Layer of reasoning using graphs
For queries that require reasoning about structural relationships or the synthesis of distributed information, a reasoning layer based on knowledge graphs explores explicit relational structures, performs agentic searches with adaptive strategies that include iterative reformulation and cross-validation, and explicitly detects and signals the contradictions that appear when multiple sources offer conflicting information, rather than hiding those inconsistencies through an artificial synthesis.
4.6. Enhanced and finely optimized generation layer
The optimized augmented generation layer takes advantage of cached generation to gain speed and computational efficiency. In addition, it uses specialized prompts that are adapted to the type of query and the level of criticism of the requested information.
This layer includes explicit source citation instructions, so that each statement can be traced to specific retrieved fragments. Finally, it incorporates sophisticated uncertainty management that clearly separates information known with high confidence, tentative data that requires verification, and what the agent explicitly recognizes as unknown.
4.7. Stratum dedicated to assessment and continuous optimization
Ultimately, a layer dedicated to evaluation and continuous improvement monitors the quality of responses in production using automated metrics.
It then automatically generates synthetic evaluation data that allows us to systematically measure recall and accuracy, without the need for costly manual labeling.
In addition, it detects error patterns that indicate specific areas that require optimization and facilitates incremental configuration adjustments, based on feedback from actual use of the agent.
4.8. Modularity and independence between layers
Each layer can be implemented and tuned independently, allowing the agent to evolve continuously without the need for total reconstruction.
The real transformation, however, lies in the sophisticated integration between layers—where each one provides information that the following use to refine their processing—and that is precisely what turns the isolated components into a cohesive business system that far exceeds the mere sum of its parts.
4.9. Key configuration decisions
The optimal configuration of these architectures for specific use cases requires well-informed technical decisions that have a decisive impact on final accuracy: determining the appropriate combination of weights by merging vector and lexical search results for the organization's own documentary corpus; choosing the similarity threshold that maximizes recall without unduly compromising accuracy;
The configuration should reflect the fragmentation strategy that best suits the types of documents predominant in the base—whether fixed-size fragmentation, recursive, semantic, or conscious of the document structure—The reordering model that stands out in the specific domain of corporate documentation must be selected; metadata and knowledge graphs should be structured so that their usefulness is maximum; and customize the language model prompts for particular use cases, balancing the needs for detail and conciseness depending on the context.
5. Business Use Cases with Operational Transformation
5.1. Professional Services and Strategic Consulting
In the strategic consulting and professional services sector, the organization's most valuable asset is accumulated knowledge — proven methodologies, previous market analysis, industry best practices and documented success stories — and, although consultants often invest substantial time searching for information that the company already has before being able to apply it to the problems of current clients, business RAG agents are radically transforming the corporate knowledge economy.
5.2. Substantial shortening of search time
Consultants that have already implemented advanced architectures on documentary repositories accumulated over years of projects claim to have cut between 50% and 70% of the time they previously spent searching for critical information.
This savings translates directly into more billable hours per professional and, crucially, into the ability to respond to requests for proposals with a speed and depth that competitors without these tools simply cannot match (Databricks, 2024).
5.3. Technical Support: How to Avoid Unnecessary Escalations
In technical support and customer service companies that manage complex products with overwhelming documentation, first-line agents are often forced to escalate difficult queries to specialists, as locating specific data within huge knowledge bases is too slow during real-time interactions.
Next-generation RAG agents are transforming that dynamic: they reduce unnecessary escalations by 40 percent to 60 percent by giving first-line agents immediate access to information that previously only specialists could quickly extract.
5.4. Boosting customer satisfaction
Not only does this mitigate the burden on specialized teams, allowing them to focus on truly complex problems, but it also significantly enhances customer satisfaction metrics, as resolution time is significantly shortened (AWS Research, 2024).
5.5. Ensuring regulatory compliance in regulated sectors
In regulated sectors such as financial services, pharmaceuticals or food where strict regulatory compliance is imperative and where verifying regulatory requirements can consume a lot of time for qualified professionals, RAG agents specialized in sector regulations are accelerating internal auditing processes and reducing the risks of non-compliance.
5.6. Boosting the speed of internal audits
A financial services company found that, after deploying an advanced RAG architecture over its regulatory corpus, the duration of its internal audits, which previously lasted several weeks, was reduced to just a tiny fraction of the original time.
5.7. Knowledge management in multinationals
When a multinational with operations spread across different regions is faced with corporate knowledge management, its employees—located in several countries and departments—require reliable access to policies, operating procedures and technical documentation that is often dispersed among multiple systems and in different languages.
In this scenario, RAG agents with native multilingual capabilities are, for the first time, offering truly unified access.
5.8. Reclaiming time and strengthening consistency
Several companies point out that their employees are demanding a significant fraction of the time that, until now, was spent researching data; calculations place this savings at around six to nine hours per week for each knowledge professional, time that is re-channeled into value-added tasks.
In addition, these entities have found that the coherence of distributed information increases significantly when everyone consults a single source instead of resorting to local copies that may be out of sync.
5.9. Competitive advantage derived from quick decision-making
Beyond the operational efficiency that can be measured, there is a less visible but, in the long run, much more valuable strategic benefit: the speed of informed decisions and the quality of their execution.
In intensely competitive market environments, the agility to address business opportunities—requests for proposals, tenders, and inquiries from potential customers—several days ahead of the competition can be the factor that defines the winner.
6. Data Sovereignty and Regulatory Compliance
6.1 Why Data Sovereignty Has a Crucial Value
For European organizations, especially those that operate in regulated sectors or handle sensitive information, the physical location of data processing and strict compliance with the General Data Protection Regulation are not mere technical details, but unavoidable legal obligations.
6.2. Architectures that minimize transfers
RAG architectures in the business environment can be deliberately planned to limit the transfer of sensitive data outside European jurisdictions:
The entire process of document extraction, fragmentation, generation of vector embeddings and storage in vector databases can be carried out entirely in a European infrastructure — either in our own data centers or through cloud computing providers with a verifiable physical presence in the European Union — so that only the fragments identified as relevant to a specific query (and not the entire set of documents) are then sent to the language models to generate the response.
6.3. Architecture suitable for regulatory compliance
By adopting a minimum transfer architecture, the exposure of sensitive data is drastically reduced and compliance with the GDPR becomes much easier, as it allows us to document precisely what information is being transferred, where it arrives and under what safeguards.
6.4. Modularity of the architecture for vendor replacement
In essence, a well-conceived business architecture needs to be modular with respect to the choice of language model.
The layer responsible for retrieving, reordering and managing knowledge can be created without depending on the specific language model used for the generation, which opens up the possibility of alternating between several providers —or moving to local solutions—without redoing the entire system.
6.5. Multilingual knowledge management
In multinational organizations, knowledge management agents must be multilingual. This involves a cross-language search, so that a query written in Spanish automatically locates relevant documents in English and, conversely, a question in English returns results in Spanish without the user having to indicate the language.
The answers are generated in the language that is contextually most appropriate — usually the language of the query — even when the sources consulted are in another language. In addition, technical terminology is intelligently preserved in its original language whenever translating it could create ambiguity or lose precision.
6.6. Requirements that robust multilingual models must meet
There are already embedding and language models that offer robust multilingual support and operate effectively in production environments, although their adoption requires careful selection and configuration.
7. Measurement, Evaluation and Optimization
7.1. The imperative to measure systematically
In reality, what separates RAG agents who continue to improve from those who remain stuck after the initial phase of implementation is the organization's degree of commitment to thorough measurement, systematic evaluation and optimization based on empirical evidence, rather than relying on intuitions about what should supposedly work.
7.2. Limitations of manual evaluation when carried out on a large scale
Although manual evaluation of the quality of responses provides valuable qualitative insights, it becomes impractical as a primary methodology when faced with agents who handle hundreds or thousands of inquiries weekly.
7.3 Synthetic Recall: Automatic Question Generation
Business agents, therefore, apply an automated evaluation based on several complementary metrics: the synthetic recall, generated automatically, consists of the fact that for each important document the system asks questions that that document should answer and, subsequently, it calculates the percentage of times that, by asking these questions, it manages to correctly locate the document. A drop in synthetic recall indicates a degradation of search capacity.
7.4. Review and follow up on each statement to the original source
This automatic groundedness verification process reviews, one by one, the statements that appear in the generated responses, trying to link them to specific fragments recovered; when the correspondence is not achieved, it is interpreted as a possible hallucination, a sign that the model is producing information without support in sources.
7.5. Comprehensive AWS frameworks for evaluation
The Amazon Web Services research team has developed highly detailed evaluation frameworks for RAG agents, incorporating three key metrics: contextual fidelity —how precisely the answers reproduce the information in the source documents without distortions or inventions—, context relevance —how suitable are the retrieved fragments to address the specific query— and completeness of response —if the answer covers all the necessary information or, on the contrary, leaves out critical elements— (AWS, 2024).
7.6. Detailed visibility for targeted optimization
By applying these frameworks with the greatest rigor, you get a detailed view of the exact places where the system reveals its strengths and weaknesses, allowing for precise and targeted optimization.
7.7. Perpetual refinement through iterative loops based on evidence
Organizations that invest in evidence-based measurement and optimization see the greatest improvements in the accuracy of their agents during the first months of operation.
These organizations, as they identify and resolve specific problems, adjust configurations for the particular documentary corpus and real use cases of their organization; they also refine prompts based on analysis of observed errors.
This continuous improvement transforms initially good agents into excellent solutions specifically optimized for the organization's unique needs.
8. Architecture as a determining factor
8.1. You can choose architecture, but you can't have everything
You can choose simplified architectures that achieve moderate accuracy at first but that stagnate or degrade as the document base grows.
Alternatively, you can choose sophisticated business architectures that integrate hybrid vector‑lexical search, specialized reordering, knowledge graphs, agentic search, conscious semantic fragmentation, and embeddings optimized for specific domains, maintaining an accuracy greater than ninety percent regardless of the increase or complexity of the knowledge base.
8.2. Architecture versus commoditized technology
The essential difference does not lie in the use of artificial intelligence — an increasingly commoditized capacity — but in concrete architectural decisions: which technologies to integrate and how to configure them optimally to adapt to the particularities of corporate documentation, priority use cases and the organization's accuracy requirements in the face of latency.
8.3 Measurable impact of each technical decision
Every technical decision—from the selection of embedding models to the definition of fragmentation strategies, to fusion algorithms in a hybrid search and the design of knowledge graph structures—measurably impacts final accuracy.
The right answers, however, are not a constant; they depend on the specific context of each organization. Therefore, a specialized understanding is needed that goes beyond familiarity with isolated technologies and that encompasses a deep understanding of how all these pieces interact within complex agents, operating under real conditions.
9. Conclusion
The imperative is not only technological, but strategic: in an economy where corporate knowledge is an essential competitive asset, organizations that master agile and reliable access to that knowledge can respond to business opportunities more quickly and with greater depth of information;
These organizations can make stronger decisions by having relevant and reliable information instantly; they scale their operations without knowledge management costs increasing proportionately. In this way, they establish sustainable competitive advantages that expand over time.
iAutomator: Corporate Knowledge Management and Automation
Email: contact@iautomator.net
Telephone: +34 689 395398
Bibliographic References
Amazon Web Services. (2024). “Evaluate the reliability of Retrieval Augmented Generation applications using Amazon Bedrock”. AWS Machine Learning Blog.
Cisco Research. (2024). “The benefits of retrieval-augmented generation for knowledge-intensive NLP tasks”. Cisco AI Research Publications.
Cohere Research. (2024). “Advanced reranking techniques for enterprise RAG systems”. See AI Technical Reports.
Databricks. (2024). “Long Context RAG Performance of LLMs”. Databricks Research.
GigaSpaces. (2024). “How Does the Quality of Internal Knowledge Bases Impact RAG Hallucinations”. GigaSpaces Research Papers.
IBM Research. (2024). “Enhancing RAG performance with smart chunking strategies”. IBM Research Blog.
Liu, Jason. (2024). “Beyond Chunks: Why Context Engineering is the Future of RAG”. Personal Blog: JXNL.co/WritingLiu, Jason. (2024). “RAG is more than just embedding search”. Personal Blog: jxnl.co/writing
Microsoft Research. (2024). “GraphRag: A Modular Graph-based Retrieval-Augmented Generation System”. Microsoft Research Publications.
Toloka Research. (2024). “RAG evaluation: a technical guide to measuring retrieval-augmented generation”. Toloka AI Technical Documentation.
Weaviate Research. (2024). “Domain-specific embeddings for enterprise search applications”. Weaviate Technical Blog.
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