Beyond the Keywords: How Retrieval Impacts AI Search Results

by

Author: Denis Avetisyan

A new study reveals that the method used to retrieve information within Azure AI Search dramatically affects the quality of AI-powered responses.

Comparative analysis demonstrates the significant impact of retrieval methods on accuracy and relevance in Azure AI Search-based Retrieval Augmented Generation (RAG) systems for eDiscovery and other applications.

While legal teams increasingly leverage artificial intelligence for efficient document review, the effectiveness of these tools hinges on accurate information retrieval. This need is addressed in ‘A Comparative Study of Retrieval Methods in Azure AI Search’, which evaluates various retrieval strategies within Microsoft Azure’s Retrieval-Augmented Generation (RAG) framework for early case assessment in eDiscovery. Our findings demonstrate that the choice of retrieval method-spanning keyword, semantic, vector, and hybrid approaches-significantly impacts the accuracy and relevance of AI-generated responses. As legal practitioners adopt RAG configurations, how can they best optimize retrieval methods to ensure transparency and maximize the utility of AI-driven insights?

Unmasking the Illusion: LLMs and the Pursuit of Verifiable Truth

Despite their remarkable ability to generate human-quality text, Large Language Models (LLMs) frequently exhibit a perplexing flaw: hallucination. This isn’t a matter of conscious deception, but rather the generation of statements that are factually incorrect, nonsensical, or not supported by their training data. These models, trained to predict the most probable continuation of a text sequence, can confidently articulate plausible-sounding falsehoods, often weaving them seamlessly into otherwise coherent responses. The phenomenon arises because LLMs prioritize statistical fluency over factual accuracy; they excel at sounding correct, even when demonstrably wrong. This presents a significant challenge, particularly in applications demanding reliability, such as medical diagnosis, legal reasoning, or scientific research, where the uncritical acceptance of LLM outputs could have serious consequences. Understanding the root causes of these hallucinations – including biases in training data, limitations in knowledge representation, and the inherent probabilistic nature of language generation – is crucial for mitigating their impact and building more trustworthy artificial intelligence systems.

Conventional approaches to knowledge integration often fall short when applied to Large Language Models, resulting in outputs detached from verifiable facts. These methods, such as simply increasing the size of training datasets or relying on statistical correlations within text, struggle to impart true understanding or differentiate between plausible-sounding statements and established truths. Consequently, LLMs frequently generate responses that, while grammatically correct and contextually relevant, are demonstrably false or lack supporting evidence. This unreliability severely limits their deployment in domains demanding accuracy – including healthcare, legal analysis, and scientific research – where even subtle inaccuracies can have significant consequences. The challenge lies not merely in accessing information, but in equipping these models with the capacity to critically evaluate and ground their responses in a robust, verifiable knowledge base.

Retrieval-Augmented Generation: Grounding Models in Reality

Retrieval-Augmented Generation (RAG) addresses limitations in Large Language Models (LLMs) by supplementing their pre-trained knowledge with information retrieved from external sources. This process involves querying a knowledge base – such as a vector database or search index – to identify documents relevant to a user’s prompt. These retrieved documents are then incorporated into the prompt provided to the LLM, effectively grounding the model’s response in factual data. By providing a verifiable source of truth, RAG significantly reduces the occurrence of hallucinations – the generation of factually incorrect or nonsensical information – and improves the overall reliability and accuracy of LLM outputs. The retrieved context allows the LLM to base its answers on current and specific information, rather than relying solely on potentially outdated or incomplete data from its training set.

Azure AI Search serves as the information retrieval component within Retrieval-Augmented Generation (RAG) systems, responsible for identifying documents relevant to a given query. It utilizes a variety of search techniques, including keyword search, semantic search, and vector search, to match queries against a knowledge base. Keyword search relies on lexical matching of terms, while semantic search understands the intent and meaning behind the query to find conceptually similar documents. Vector search, powered by embedding models, represents both queries and documents as vectors in a high-dimensional space, enabling the retrieval of documents based on semantic similarity even if they don’t share keywords. The service supports multiple data sources and provides features like customizable scoring and filtering to refine search results and improve the accuracy of the RAG pipeline.

Document chunking is a critical preprocessing step in Retrieval-Augmented Generation (RAG) pipelines due to the input token limits of Large Language Models (LLMs) and the need for precise information retrieval. Large documents are divided into smaller, manageable segments – or chunks – to facilitate efficient processing and reduce the computational cost associated with transmitting extensive contexts to the LLM. The size of these chunks, and the method used to create them-such as fixed-size windows, semantic chunking, or recursive character text splitting-directly impacts retrieval performance; smaller chunks increase precision but may lack sufficient context, while larger chunks improve recall at the risk of including irrelevant information. Optimal chunking strategies balance these trade-offs, maximizing the likelihood of retrieving relevant passages while remaining within the LLM’s token constraints and minimizing noise.

Beyond Keywords: Unlocking Meaning with Advanced Retrieval

Traditional keyword search relies on exact matches between query terms and document content, limiting retrieval to instances where the same words are used. In contrast, Semantic Search and Vector Search methods address this limitation by focusing on the meaning of the query and documents. These techniques utilize models to create numerical representations, or embeddings, of text, capturing contextual relationships between words. By comparing the embeddings of the query and documents, these search methods can identify conceptually similar content even if the exact keywords are not present, leading to improved recall and the discovery of more relevant information. Vector Search specifically utilizes vector databases to efficiently store and compare these embeddings, enabling rapid similarity searches at scale.

Hybrid Search operates by integrating keyword-based retrieval with vector search techniques to improve information retrieval performance. Keyword search identifies documents containing the exact terms specified in a query, maximizing precision but potentially missing conceptually similar results. Vector search, utilizing text embeddings, identifies documents with similar meaning, increasing recall. By combining these approaches, Hybrid Search aims to capitalize on the strengths of each method; keyword search rapidly narrows the search space to highly relevant documents, while vector search expands the results to include semantically related content that a strict keyword match might overlook. This dual strategy generally results in a higher overall recall and precision compared to either keyword or vector search used in isolation.

Hybrid-Semantic Search improves retrieval accuracy by combining initial search results – derived from techniques like keyword or vector search – with a semantic reranking step. This reranking utilizes Text Embeddings, which are numerical representations of textual meaning, allowing the system to assess the semantic similarity between the query and each document. Documents are then reordered based on this similarity, prioritizing those most relevant to the query’s intent, not just keyword matches. Evaluation within Azure Retrieval Augmented Generation (RAG) demonstrates that this method consistently yields more relevant results compared to other retrieval approaches, directly impacting the quality and accuracy of AI-generated responses.

RAG in the Legal Realm: Reconstructing Truth from Data

The process of eDiscovery, and particularly Early Case Assessment (ECA), is being fundamentally reshaped by Retrieval-Augmented Generation (RAG). Historically, legal teams faced substantial challenges in sifting through massive datasets – emails, documents, and more – to pinpoint crucial evidence. RAG addresses this bottleneck by swiftly retrieving relevant information from these datasets and providing it to large language models. This allows the LLM to generate summaries, identify key themes, and flag potentially critical documents with unprecedented speed and accuracy. Rather than relying solely on the LLM’s pre-existing knowledge, RAG grounds its responses in concrete evidence, transforming ECA from a laborious, manual process into a dynamic, data-driven one. The result is a significantly reduced timeframe for case evaluation, improved identification of relevant materials, and ultimately, a more efficient and cost-effective legal strategy.

Retrieval-Augmented Generation (RAG) dramatically improves the trustworthiness of legal analyses by directly linking large language model (LLM) conclusions to specific source materials. Instead of relying solely on the LLM’s pre-existing knowledge-which may be outdated, incomplete, or even biased-RAG compels the model to base its responses on a curated set of retrieved evidence. This grounding is critical for defensibility in legal contexts, providing a clear audit trail that demonstrates how a particular finding was reached. By citing the relevant passages used to formulate its conclusions, the LLM minimizes the risk of hallucination or unsupported assertions, and significantly enhances the accuracy and reliability of eDiscovery and Early Case Assessment processes. The ability to verify the factual basis of generated insights transforms LLMs from potentially unreliable tools into robust instruments for legal reasoning.

The effectiveness of Retrieval-Augmented Generation (RAG) systems in legal contexts hinges significantly on the art of prompt engineering. Carefully crafted prompts aren’t merely instructions; they are the key to unlocking relevant insights from retrieved documents and guiding the Large Language Model (LLM) towards accurate and defensible conclusions. Sophisticated prompt design involves specifying the desired output format, defining the scope of the inquiry, and strategically incorporating contextual cues from the retrieved evidence. This process ensures the LLM doesn’t simply generate text, but synthesizes information grounded in verifiable sources, mitigating the risk of hallucination and bolstering the reliability of legal findings. Through iterative refinement and targeted instruction, prompt engineering maximizes the quality of generated insights, transforming raw data into compelling and legally sound arguments.

The study’s focus on dissecting retrieval methods within Azure AI Search echoes a fundamental principle: true understanding necessitates deconstruction. It’s not enough to simply use a system; one must probe its mechanisms, expose its vulnerabilities, and map its inner workings. As Tim Bern-Lee once stated, “The Web is more a social creation than a technical one.” This rings true within the context of RAG systems; the ‘retrieval’ component isn’t merely a technical function, but a social construct determining how knowledge is accessed and presented. The exploration of semantic versus vector search, and their impact on eDiscovery, isn’t just about improving accuracy-it’s an exploit of comprehension, revealing the underlying logic governing information retrieval and, ultimately, the quality of AI-generated insights.

What Breaks Down From Here?

This exercise in dissecting retrieval methods within Azure AI Search hasn’t delivered answers, predictably. It’s merely shifted the questions. The observed performance variance between semantic and vector search isn’t a testament to one being ‘better’, but a signal that the very notion of relevance is a moving target. What constitutes a ‘good’ retrieval in the context of eDiscovery-or any knowledge domain-depends less on the algorithm and more on the inherent messiness of information itself. A perfectly precise retrieval is, after all, a retrieval of something already known.

The next logical dismantling involves abandoning the pursuit of a universal ‘best’ method. Instead, the field should focus on creating systems that diagnose their own failures. Can a retrieval system articulate why it presented a given result? Can it quantify its own uncertainty? True intelligence isn’t about finding the needle; it’s about knowing when the haystack is rigged.

Ultimately, this isn’t about optimizing RAG; it’s about acknowledging the limitations of representation. Information isn’t ‘retrieved’ so much as constructed by the process itself. And once one accepts that construction is always a form of distortion, the real work – deconstructing that distortion – can begin.

Original article: https://arxiv.org/pdf/2512.08078.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

See also:

2025-12-11 06:05