Enhancing LLM Accuracy with Coveo Passage Retrieval on Amazon Bedrock

The landscape of artificial intelligence is evolving at an unprecedented pace, with Large Language Models (LLMs) at the forefront of this transformation. As enterprises increasingly adopt LLMs to drive innovation and efficiency, a critical challenge emerges: ensuring the accuracy and reliability of the information these models generate. Generic LLM knowledge, while vast, often lacks the specific context and factual grounding required for business-critical applications. This gap can lead to misinformation, erode user trust, and hinder the effective deployment of generative AI. To address this, Coveo has partnered with Amazon Web Services (AWS) to integrate its advanced Passage Retrieval capabilities with Amazon Bedrock, offering a powerful solution for grounding LLM responses in an organization’s own verified knowledge. This integration promises to enhance LLM accuracy, bolster user trust, and accelerate the development and deployment of trustworthy generative AI applications across industries.

The Imperative for Accurate LLM Responses in the Enterprise

The Evolving Landscape of Artificial Intelligence

The field of artificial intelligence (AI) is undergoing a profound transformation, marked by rapid advancements and increasing integration into various aspects of business and daily life. This evolution is driven by breakthroughs in machine learning, particularly in the realm of large language models (LLMs). As AI capabilities expand, so does the potential for these technologies to revolutionize industries, streamline operations, and unlock new avenues for innovation. The current year, 2025, stands as a testament to this ongoing progress, with AI no longer a futuristic concept but a tangible force shaping the present and future. Developments in AI throughout 2024 and into 2025 have seen a significant increase in enterprise adoption, with a focus on practical applications that deliver measurable business value.

The Rise of Large Language Models (LLMs)

Among the most impactful developments in AI is the emergence and rapid proliferation of Large Language Models (LLMs). These sophisticated models, trained on vast datasets, possess an unprecedented ability to understand, generate, and manipulate human language. LLMs are powering a new generation of applications, from advanced content creation tools and sophisticated chatbots to powerful analytical engines. Their capacity to process and generate text that is often indistinguishable from human output has captured the attention of businesses worldwide, promising enhanced productivity and novel user experiences. By early 2025, LLM adoption has moved beyond experimental phases into mainstream enterprise strategies.

The Critical Need for Data Foundation

Despite the remarkable capabilities of LLMs, their effectiveness in enterprise settings is fundamentally dependent on the quality and relevance of the data they access. While LLMs are trained on broad internet datasets, they often lack the specific, nuanced knowledge required for accurate and reliable responses within a particular organization’s context. This gap can lead to “hallucinations” – instances where the LLM generates plausible but incorrect or fabricated information. Consequently, establishing a robust and trustworthy data foundation is paramount for enterprises seeking to leverage LLMs effectively and responsibly. Without it, the potential benefits of LLMs are overshadowed by the risks of misinformation and eroded user trust. The quality of an enterprise’s internal data has become a key differentiator in successful AI implementation as of 2025.

Bridging the Gap: From General Knowledge to Enterprise Specificity

The challenge for enterprises lies in bridging the divide between the general knowledge embedded within pre-trained LLMs and the specific, proprietary information that resides within their own data silos. Generic LLM outputs, while impressive in their fluency, are often insufficient for business-critical tasks that demand precision, accuracy, and adherence to company policies and factual records. Effectively harnessing LLMs requires a mechanism to ground their responses in the organization’s unique knowledge base, ensuring that the generated content is not only coherent but also factually correct and contextually relevant. This need for enterprise specificity has driven the demand for advanced retrieval augmentation techniques.

Understanding Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG) has emerged as a leading paradigm for addressing the limitations of LLMs in enterprise applications. RAG systems combine the generative power of LLMs with an external knowledge retrieval component. This approach allows the LLM to access and incorporate information from a specific data source before generating a response. By retrieving relevant documents or passages, RAG ensures that the LLM’s output is informed by factual data, thereby increasing accuracy and reducing the likelihood of generating misinformation. RAG has become a standard architectural pattern for enterprise-grade generative AI solutions throughout 2024 and 2025.

The Complexity of the Retrieval Component in RAG

Within the RAG framework, the retrieval process is arguably the most complex and critical component. It is the retrieval system’s responsibility to accurately identify and extract the most pertinent information from vast and often disparate enterprise data sources. This information then serves as the context for the LLM to generate its response. An inefficient or inaccurate retrieval mechanism can undermine the entire RAG system, leading to irrelevant or incomplete information being fed to the LLM, and ultimately resulting in suboptimal or misleading outputs. Therefore, optimizing the retrieval stage is key to unlocking the full potential of RAG. The sophistication of the retrieval mechanism directly correlates with the trustworthiness of the generated AI output.

Introducing Coveo Passage Retrieval on Amazon Bedrock

Coveo’s Role as an AWS Partner

Coveo, a recognized leader in AI-powered relevance and search, has partnered with Amazon Web Services (AWS) to address the critical need for enhanced LLM accuracy. As an AWS Partner, Coveo leverages its expertise in enterprise search and information retrieval to integrate its advanced capabilities with Amazon Bedrock, AWS’s comprehensive service for building and scaling generative AI applications. This collaboration aims to provide enterprises with a robust solution that ensures LLM-generated responses are grounded in accurate, relevant, and secure enterprise knowledge. This partnership, solidified through ongoing joint development and go-to-market strategies in 2024-2025, offers a compelling integrated solution for AWS customers.

Amazon Bedrock: A Unified Platform for Generative AI

Amazon Bedrock serves as the foundational platform for this integrated solution. It offers a managed service that provides access to a range of leading foundation models from various AI companies, enabling developers to build and deploy generative AI applications. Bedrock’s capabilities extend to agents, which can orchestrate complex tasks by interpreting natural language queries and interacting with various data sources and APIs. By integrating Coveo’s retrieval technology with Bedrock Agents, organizations can create AI applications that are not only generative but also deeply informed by their specific business context. As of 2025, Amazon Bedrock continues to expand its model offerings and agent capabilities, solidifying its position as a central hub for enterprise generative AI.

The Coveo AI-Relevance Platform

At the heart of Coveo’s offering is its AI-Relevance Platform. This platform is designed to connect, unify, and intelligently surface content from a multitude of cloud and on-premises repositories. It employs sophisticated machine learning algorithms and in-depth usage analytics to continuously optimize the relevance of search results and recommendations. The platform’s ability to understand user behavior, context, and profile data allows for personalized and context-aware information delivery, a capability that is crucial for enhancing LLM performance. The platform’s continuous learning capabilities ensure that relevance is not static but dynamically adapts to evolving user needs and content landscapes.

The Passage Retrieval API: A Deeper Dive

Coveo’s Passage Retrieval API is the key innovation enabling the enhancement of LLM accuracy within the Amazon Bedrock ecosystem. This API is specifically designed to extract precise, relevant text passages from an organization’s unified knowledge base. Instead of returning entire documents, which can be overwhelming and dilute the most critical information, the API delivers targeted snippets of text. These passages are accompanied by essential metadata, such as source URLs, which are vital for enabling citations and ensuring traceability back to the original enterprise content. This focused delivery of information ensures that LLMs receive the most pertinent context for generating accurate and trustworthy responses. The API’s design prioritizes precision and context, making it an ideal component for RAG architectures.

The Core Challenge: LLM Accuracy and Trust

The Risk of Inaccurate LLM Outputs

The widespread adoption of LLMs in enterprise environments is tempered by a significant concern: the potential for inaccurate or misleading outputs. When LLMs operate solely on their pre-trained knowledge, they can generate responses that are factually incorrect, outdated, or irrelevant to specific business contexts. This phenomenon, often referred to as “hallucination,” poses a substantial risk. Inaccurate information can lead to flawed decision-making, damage customer trust, and undermine an organization’s credibility. For businesses that rely on precise information for operations, compliance, or customer service, these inaccuracies are unacceptable. Reports from 2024 highlighted that while LLM capabilities have advanced, the issue of factual accuracy remains a primary barrier to full enterprise trust.

Eroding User Trust and Organizational Credibility

Trust is a cornerstone of any successful business interaction, whether with customers, partners, or internal stakeholders. When AI-powered applications deliver unreliable information, this trust is quickly eroded. Users become hesitant to rely on the AI for critical tasks, leading to decreased adoption and a failure to realize the intended benefits of the technology. Furthermore, consistent delivery of inaccurate information can significantly damage an organization’s reputation and brand image, making it difficult to regain user confidence. Ensuring the accuracy of LLM responses is therefore not just a technical challenge but a strategic imperative for maintaining credibility. As of September 2025, maintaining user trust remains a top priority for organizations deploying AI.

The Need for Context-Aware and Grounded Responses

LLMs, by their nature, are trained on vast, general datasets. While this provides a broad understanding of language and concepts, it often lacks the specific, granular context required for enterprise applications. For example, an LLM might know about a company’s products in general terms, but it may not have access to the latest product specifications, internal support documentation, or customer-specific configurations. To be truly valuable, LLM responses must be “grounded” in the organization’s proprietary knowledge base. This means the AI should be able to access, understand, and synthesize information directly from the company’s internal documents, databases, and other authoritative sources, providing answers that are both accurate and deeply relevant to the specific query and context. This grounding is essential for enterprise-grade AI solutions.

Mitigating Risks in Regulated Industries

In highly regulated industries such as finance, healthcare, and legal services, the stakes for AI accuracy are exceptionally high. Errors in information can lead to severe compliance violations, financial penalties, and legal repercussions. LLMs used in these sectors must adhere to strict standards of accuracy, traceability, and data privacy. The ability to provide verifiable sources for AI-generated information is not just desirable but often a mandatory requirement. Solutions that can ensure LLM responses are factually sound and traceable are essential for enabling the safe and compliant adoption of AI in these sensitive domains. Regulatory bodies worldwide are increasingly scrutinizing AI deployments, emphasizing the need for auditable and accurate AI systems.

Coveo’s Solution: Passage Retrieval API Explained

The Core Functionality: Extracting Relevant Passages

The Coveo Passage Retrieval API is engineered to tackle the challenge of LLM accuracy head-on by focusing on the precision of information delivery. Instead of returning entire documents, which can contain a significant amount of irrelevant content, the API intelligently identifies and extracts specific, high-value text passages or “chunks” from an organization’s unified knowledge index. This targeted approach ensures that the LLM receives the most pertinent information needed to formulate an accurate response, significantly reducing the noise and increasing the signal-to-information ratio. This focused extraction is a key differentiator for achieving high accuracy in RAG systems.

Two-Stage Retrieval for Enhanced Precision

To achieve its high level of accuracy, Coveo employs a sophisticated two-stage retrieval process. The first stage involves identifying and filtering accessible and relevant documents from the diverse content sources within the enterprise. This broad scan narrows down the potential information pool. The second stage then delves deeper, more precisely identifying the most relevant passages within those selected documents. This meticulous, multi-layered approach ensures that the information passed to the LLM is not only relevant at a document level but also at a granular passage level, maximizing the accuracy of the final output. This advanced retrieval strategy is critical for complex enterprise knowledge bases.

Hybrid Ranking: The Power of Combined Search Methods

Coveo’s retrieval effectiveness is further amplified by its use of hybrid ranking. This strategy combines the strengths of both semantic (vector) search and lexical (keyword) matching. Semantic search excels at understanding the meaning and context of queries, even when exact keywords are not present. Lexical search, on the other hand, is highly effective at matching specific terms and phrases. By integrating these two approaches, Coveo’s system can retrieve information that is both contextually appropriate and precisely aligned with the user’s intent, ensuring that the most relevant information is surfaced, regardless of the query’s formulation. This blended approach has become a best practice for enterprise search in 2025.

Machine Learning for Continuous Relevance Optimization

A key differentiator for Coveo is its commitment to continuous improvement through machine learning (ML). The AI-Relevance Platform constantly learns from user interactions, analyzing behavior, in-app context, and profile data. This ongoing learning process allows the retrieval system to adapt and refine its results over time, becoming increasingly personalized and context-aware for each individual user’s journey. This ML-driven relevancy ensures that the information provided to LLMs is not static but dynamically tailored to deliver the best possible context for generating accurate and helpful responses. The platform’s ability to adapt ensures sustained relevance in dynamic enterprise environments.

Metadata and Citations for Traceability

Beyond just retrieving text passages, the Coveo Passage Retrieval API also provides crucial metadata alongside the retrieved content. This metadata often includes source URLs, document titles, and other relevant contextual information. This is essential for enabling citations within the LLM’s generated responses. By providing traceable links back to the original enterprise knowledge sources, users can verify the information and build confidence in the AI’s outputs. This traceability is a critical factor in establishing trust and ensuring compliance, especially in sensitive or regulated environments. The ability to cite sources is a non-negotiable requirement for many enterprise AI applications in 2025.

Technical Integration and Architecture

The Unified Hybrid Index: A Centralized Knowledge Hub

A cornerstone of Coveo’s approach is its unified hybrid index. This index serves as a centralized repository that connects and organizes structured and unstructured content from across an organization’s disparate data sources. Whether the information resides in cloud applications, on-premises servers, databases, or document management systems, Coveo’s index can ingest and unify it. This consolidation eliminates data silos and ensures that the retrieval system has a comprehensive view of the enterprise’s knowledge base, making it possible to find relevant information regardless of its original location. The unified index is crucial for providing a single source of truth for AI applications.

Maintaining Enterprise-Grade Security and Permissions

Security and access control are paramount in enterprise environments, especially when dealing with sensitive information. Coveo’s solution is built to respect and enforce enterprise-grade security at every level. It achieves this by importing item-level permissions from the original content sources during the indexing process through an early-binding approach. This ensures that when passages are retrieved, they are strictly filtered based on the user’s existing access rights. This robust permission model guarantees that generative AI applications only surface information that the user is authorized to see, mitigating security risks and ensuring compliance with internal policies and external regulations. This adherence to existing security protocols is vital for enterprise adoption.

Amazon Bedrock Agents: Orchestrating the AI Workflow

Amazon Bedrock Agents play a crucial role in orchestrating the interaction between users, LLMs, and Coveo’s retrieval capabilities. Agents are designed to interpret natural language queries, understand user intent, and determine the appropriate actions to take. In this integrated solution, the Bedrock Agent is configured to recognize when a query requires information retrieval. It then intelligently triggers Coveo’s Passage Retrieval API to fetch the necessary context. This seamless orchestration ensures that the LLM receives the right information at the right time to generate a grounded and accurate response. Bedrock Agents provide the connective tissue that makes complex RAG architectures manageable.

Action Groups and OpenAPI Specifications

The integration between Amazon Bedrock Agents and Coveo’s API is facilitated through the concept of “Action Groups.” An Action Group defines the specific API operations that a Bedrock Agent can invoke. By using standard OpenAPI specifications, Coveo’s Passage Retrieval API operations are clearly defined, providing a structured interface for the Bedrock Agent. This allows the agent to understand how to call the API, what parameters to pass, and how to interpret the results. This structured approach ensures a reliable and efficient communication channel between the agent and the retrieval service. The use of industry-standard specifications simplifies integration and reduces development time.

Enforcing Security and Compliance in Retrieval

The seamless integration of Coveo’s security model with Amazon Bedrock Agents ensures that compliance is maintained throughout the entire process. When an agent retrieves passages via Coveo’s API, the results are already filtered according to the user’s permissions. This means that the LLM receives context that is not only relevant but also secure and compliant. This end-to-end security enforcement is critical for enterprises, particularly those operating under strict regulatory frameworks, as it minimizes the risk of data breaches or unauthorized information disclosure. This integrated security posture is a key enabler for regulated industries adopting generative AI.

Benefits of the Coveo-Bedrock Partnership

Enhanced LLM Accuracy and Reliability

The primary benefit derived from the integration of Coveo Passage Retrieval with Amazon Bedrock is a significant enhancement in LLM accuracy and reliability. By providing LLMs with precise, context-aware passages from an organization’s trusted knowledge base, the likelihood of generating incorrect or fabricated information is drastically reduced. This grounding in factual enterprise data ensures that the AI’s outputs are dependable, leading to more trustworthy and effective AI-powered applications. This improvement in accuracy is fundamental to realizing the business value of generative AI.

Increased User Trust and Confidence

When AI applications consistently deliver accurate and relevant information, user trust and confidence soar. The ability to provide citations and traceable sources for generated answers further bolsters this trust. Users are more likely to engage with and rely on AI tools when they know the information is verifiable and grounded in authoritative enterprise knowledge. This improved trust translates into higher adoption rates for AI solutions and a more positive overall user experience. As of 2025, trust is a critical factor in the long-term success of any AI deployment.

Reduced Risk of Misinformation and Hallucinations

The sophisticated retrieval mechanisms employed by Coveo, including its two-stage process and hybrid ranking, are highly effective at mitigating the risks associated with LLM hallucinations. By precisely identifying and delivering the most relevant information, the system minimizes the chances of the LLM generating plausible but incorrect statements. This reduction in misinformation is crucial for maintaining brand reputation, ensuring sound decision-making, and avoiding costly errors, especially in high-stakes business environments. Proactive mitigation of hallucinations is a key focus for enterprise AI strategies in 2024-2025.

Accelerated Development and Time to Value

The partnership between Coveo and AWS provides pre-built examples and robust APIs that accelerate the development of generative AI applications. Developers can leverage the combined power of Amazon Bedrock’s managed infrastructure and Coveo’s advanced retrieval capabilities to build sophisticated AI solutions more rapidly. This not only speeds up the development cycle but also allows organizations to realize the business value of their AI investments much faster. The availability of integrated solutions significantly lowers the barrier to entry for complex AI projects.

Unified and Secure Access to Enterprise Knowledge

Coveo’s unified hybrid index and robust security model ensure that enterprises can provide secure, permission-aware access to their knowledge base through AI applications. This eliminates the need for complex, custom integrations to access disparate data sources while maintaining strict control over who can access what information. The result is a streamlined, secure, and compliant way to leverage the organization’s collective intelligence. This unified approach simplifies data management and enhances security posture.

Continuous Improvement Through AI and Analytics

The Coveo AI-Relevance Platform’s inherent machine learning capabilities mean that the retrieval system continuously learns and improves over time. Furthermore, the platform offers analytics and insights into how generated answers perform, identifying areas where information might be missing or underutilized. This data-driven feedback loop allows organizations to fine-tune their content and AI applications, driving measurable business impact and ensuring ongoing optimization of AI-driven experiences. Continuous improvement is essential for maintaining the effectiveness of AI systems in dynamic business environments.

Real-World Applications and Use Cases

Intelligent Virtual Assistants and Chatbots

One of the most immediate and impactful applications is the enhancement of virtual assistants and chatbots. By integrating Coveo Passage Retrieval with Amazon Bedrock Agents, organizations can power chatbots that provide highly accurate and contextually relevant answers to customer inquiries, employee questions, or support requests. These AI assistants can access and synthesize information from internal knowledge bases, product documentation, and support articles, offering a superior self-service experience and freeing up human agents for more complex issues. As of 2025, advanced chatbots are a key component of customer service and internal support strategies.

Internal Knowledge Management and Employee Support

Enterprises can leverage this integrated solution to build powerful internal knowledge management tools. Employees can ask natural language questions and receive precise answers grounded in company policies, HR information, technical documentation, and best practices. This not only improves employee productivity by reducing the time spent searching for information but also ensures consistency and accuracy in the dissemination of internal knowledge, supporting onboarding, training, and ongoing operations. Enhanced knowledge access is a significant driver of employee efficiency.

Customer Support Copilots

Customer support teams can benefit immensely from AI copilots that assist human agents during customer interactions. These copilots, powered by Coveo and Bedrock, can quickly retrieve relevant information from a vast knowledge base in real-time, providing agents with accurate answers, troubleshooting steps, and relevant product details. This empowers agents to resolve customer issues faster and more effectively, leading to improved customer satisfaction and reduced support costs. Copilot solutions have seen rapid adoption in customer-facing roles throughout 2024.

Content Generation and Summarization Tools

While LLMs are adept at content generation, ensuring the factual accuracy of that content can be challenging. By using Coveo Passage Retrieval, content creators can leverage LLMs that are grounded in verified enterprise data. This allows for the generation of accurate marketing copy, product descriptions, technical documentation, and reports that are factually sound and aligned with company messaging and data. The ability to summarize complex documents with accurate, sourced information is also a significant advantage. This capability is crucial for maintaining brand integrity and accuracy in external communications.

Compliance and Regulatory Assistance

In regulated industries, the ability to provide accurate, traceable information is critical. AI applications built using this integration can assist with compliance-related tasks by retrieving and presenting accurate information from regulatory documents, legal precedents, or internal compliance guidelines. The citation capabilities ensure that all information provided can be traced back to its authoritative source, supporting audit trails and ensuring adherence to regulatory requirements. As regulatory scrutiny of AI increases, these capabilities become indispensable.

The Future of Generative AI with Enhanced Retrieval

Towards More Trustworthy and Explainable AI

The integration of sophisticated retrieval mechanisms like Coveo’s with foundational LLM platforms like Amazon Bedrock marks a significant step towards creating more trustworthy and explainable AI systems. By ensuring that AI responses are grounded in verifiable data and providing clear citations, these solutions move beyond the “black box” nature of some AI models. This transparency is crucial for building confidence and enabling wider adoption of AI across all sectors. The trend towards explainable AI (XAI) is a defining characteristic of the current AI development cycle.

Personalized and Context-Aware AI Experiences

As AI systems become more adept at understanding individual user contexts through continuous learning and ML-driven relevancy, the experiences they provide will become increasingly personalized. The ability to retrieve highly specific information tailored to a user’s role, past interactions, and current needs will enable AI applications to offer truly bespoke assistance and insights, making them indispensable tools for productivity and decision-making. Personalized AI experiences are expected to become the norm across various applications by 2026.

Democratizing Access to Enterprise Knowledge

By unifying disparate data sources and providing a secure, intelligent interface, this partnership effectively democratizes access to an organization’s collective knowledge. Employees at all levels can leverage AI to tap into expertise and information that might have previously been difficult to find or only accessible to a select few. This fosters a more informed and collaborative work environment. The ability to surface relevant information easily empowers a broader range of employees to make data-driven decisions.

The Evolution of Agentic AI

The advancements in grounding LLM responses and orchestrating complex tasks via agents point towards a future where agentic AI plays an even more prominent role. AI agents capable of understanding nuanced requests, retrieving precise information, and taking appropriate actions will become increasingly sophisticated. This will enable more autonomous and intelligent systems that can manage complex workflows, automate intricate processes, and provide proactive support across various business functions. The development of more capable AI agents is a key area of focus for AI research and development in 2025.

Continuous Innovation in AI-Powered Relevance

The ongoing development in AI, particularly in areas like natural language understanding, semantic search, and generative models, promises continuous innovation. The partnership between Coveo and AWS is well-positioned to capitalize on these advancements, constantly refining and enhancing the capabilities of AI-powered relevance and retrieval. This ensures that enterprises can stay at the forefront of AI adoption, leveraging the latest technologies to drive business value and maintain a competitive edge. The rapid pace of innovation in AI means that solutions must be adaptable and continuously updated to remain effective.