The Role of Retrieval Augmented Generation (RAG) in Development of AI-Infused Enterprise Applications

Introduction

Artificial Intelligence (AI) is transforming enterprise applications, enabling businesses to enhance efficiency, improve decision-making, and unlock new opportunities. However, AI adoption is not a one-size-fits-all approach—organizations integrate AI at different levels depending on their needs, existing infrastructure, and strategic goals.

This article explores three categories of AI-infused applications, their enterprise use cases, and how Retrieval-Augmented Generation (RAG) is revolutionizing AI adoption by improving accuracy, relevance, and contextual understanding in AI-driven applications.

AI-Infused Application Categories

Enterprises integrate AI into their applications in different ways, ranging from enhancing existing software to creating entirely new AI-driven systems. The three primary categories of AI-infused applications are:

1. AI-Embedded
2. AI-Assisted
3. AI-Centric

The below diagram illustrates the transformation of the enterprise application landscape as it evolves with the adoption of AI, involving both the modernization of existing applications to incorporate AI and the development of new autonomous, AI-centric applications.

1. AI-Embedded Applications

These applications are traditional enterprise systems enhanced with AI capabilities to improve performance, efficiency, and automation. AI plays a supporting role by optimizing existing workflows without fundamentally changing the core functionality. This help organizations in incremental AI adoption, reduced operational costs and improved workflow efficiency.

Below are key use cases across industries:

• Customer Relationship Management (CRM) Optimization: AI-powered lead scoring (e.g., Salesforce Einstein) prioritizes potential customers based on predictive models.
•  Fraud Detection in Banking: AI detects anomalous transactions by analyzing vast amounts of historical financial data.
• Enterprise Search Enhancement: AI improves search results using natural language understanding (NLU) and semantic search within document management systems.
• IT Operations (AIOps): AI predicts IT failures and automates incident response.
• Personalized E-commerce Experiences: AI recommends products based on browsing behavior and purchase history.

2. AI-Assisted Applications

In these applications, AI works alongside human users, augmenting decision-making and automating tasks while still allowing human oversight. Unlike AI-embedded applications, AI is more deeply intertwined with the core functionality, leading to collaborative intelligence between humans and AI.

Below are key use cases across industries: 

• Financial Risk Analysis: AI retrieves and analyzes real-time market data to assist analysts in evaluating risks.
• AI-Augmented Software Development: AI-powered coding assistants (e.g., GitHub Copilot) suggest and auto-correct code based on project context.
• AI-Powered HR Recruiting: AI screens resumes and recommends top candidates based on hiring trends.
• Legal Document Review: AI extracts key clauses, identifies legal risks, and summarizes contracts.
• Healthcare AI Decision Support: AI analyzes medical images and assists radiologists in identifying anomalies.

3. AI-Centric Applications

These are applications where AI is the primary driver of functionality, with traditional software playing a supporting role. AI dictates how the application operates, and the entire system is built around AI capabilities.

Below are key use cases across industries: 

• Autonomous Customer Support Agents: AI chatbots handle customer queries without human intervention.
• AI-Powered Financial Robo-Advisors: AI manages investment portfolios based on real-time financial trends.
• AI-Based Supply Chain Optimization: AI predicts demand, optimizes logistics, and prevents disruptions.
• AI-Driven Content Generation: AI creates personalized marketing content, blog posts, and ad campaigns.
• AI-Powered Cybersecurity Systems: AI autonomously detects and neutralizes security threats.

How RAG (Retrieval-Augmented Generation) Enhances AI-Infused Applications

As described in above section, the adoption of AI follows a continuum—enterprises may start by embedding AI into existing applications, move towards AI-assisted workflows, and eventually develop AI-centric applications.

One of the biggest challenges of AI adoption is ensuring accurate, reliable, and contextually relevant outputs. Traditional AI models, including Large Language Models (LLMs), often struggle with hallucinations (generating incorrect information) and outdated knowledge. This is where Retrieval-Augmented Generation (RAG) becomes a game-changer.

RAG improves AI performance by combining real-time information retrieval with generative AI. Instead of relying solely on pre-trained knowledge, RAG-based systems dynamically retrieve relevant data from enterprise sources before generating responses.

This section introducing a spectrum of RAG implementation patterns—from Naive RAG to Advanced and Agentic RAG—that organizations can leverage based on their maturity and needs.

Naïve RAG

The basic stage of RAG pattern comprises of laying strong foundation and initial implementation. This starts with understanding the RAG fundamentals and its core components. The core components of RAG include:

Data Management 

This component is responsible for managing the input data for the application. It begins by ingesting documents, which can be in various formats such as text, images, or audio. These documents are then stored in a cloud object storage system or persistent storage system, which provides a scalable and durable storage solution.

Data Processing 

Once the documents are stored, a message bus is used to trigger the parsing process. This process involves breaking down the documents into smaller chunks, extracting metadata, and generating embeddings. Embeddings are vector representations of the documents that capture their semantic meaning. They are crucial for tasks such as semantic search and response generation.

The vectorized documents are then saved in a vector database, which is optimized for storing and querying vector data. 

Conversational Interface / UI

This component provides the user interface for interacting with the application. It enables users to perform Q&A sessions, converting user queries into embeddings for semantic search. This allows the application to retrieve relevant document chunks based on the user's query. 

Retrieval Module

This component is responsible for identifying and fetching relevant pieces of information from a large dataset or knowledge base that are saved in the vector database as described in above Data Processing module. It typically uses techniques like vector search, dense passage retrieval, or traditional keyword-based search to find the most relevant documents or text snippets that relate to the query or prompt. 

The retrieved context and the original user query are then sent to the generation module for response generation. < any difference between retrieval of un/structured data>

Generation Module

Once relevant information is retrieved, the generator module which consists of generative model or large language model, process the retrieved information and generates the contextual appropriate response. In this component, selecting right transformed-based model is important and at the same time retrieved data to ensure the output is both accurate and informative.

Below diagram describes the high-level architecture & process flow to build the basic RAG application.

Intermediate RAG

The intermediate stage refers to enhancing capabilities and expanding its use case. In the intermediate RAG implementation consists of all core components of the RAG, and additional components aligning to organizations goal. Below are the suggested additional components that can be added to enhance core RAG implementation.

Additional Component Details

Cache Module

This component is responsible for caching the prompts and its response. The goal is to optimizes the number of API calls to the large language models by serving similar requests from the cached response, resulting in reduced latency, lower cost, and enhance application efficiency.

Model Evaluation 

The model evaluation component responsible for calculating relevant metrics which are essential to assess the quality of generated content, its relevance in answering business queries from the users. This also standardizes the process of evaluating the certain types of models, and similar functions can be called to keep consistency across various applications. 

Ethical Compliance 

This component ensures that the content generated from the LLM backed applications is complaint with ethical aspects, both legally and socially. The responses should not experience disparity across segments of gender, age, race etc. There are acceptable metrics like Disparate Impact Ratio, Demographic parity which can be employed to quantify the ethical compliance of the models.  

Feedback 

This component also known as ‘human-in-the-loop’ (HITL) paradigm is used to enhance system performance and reliability by incorporating human supervision. Users can provide feedback on retrieved context for relevancy and generated response for accuracy. This feedback will used to enhance the retrieval and generation process.

Below diagram describes the high-level architecture to build the intermediate level RAG application.

Advance RAG

The advance stage refers here to incorporate advance capabilities that can adapt to changing data and surge of models, support muti-modal requirement, governance requirement and cross functional collaboration. The advance stage includes core and intermediate capabilities and additional advance capabilities aligning to the organization’s goal and roadmap. Below are the suggested additional components that can be added to build advance RAG solutions.

Model Gateway

This component will act as middleware to integrate model with business application, and it will employee many capabilities such as caching, routing, load balancing and analytics:

Regulatory Compliance 

This module ensures that system adheres to legal, industry specific standards. This becomes particularly critical in industries like healthcare (HIPAA), finance (GDPR, SOX), etc. It can start at the input data checks where system identifies and redacts sensitive information. Based on the defined RBAC, system can also keep audit trail of interactions, and decision points for traceability. This is critical to build trust in the application demonstrating adherence to regulatory bodies. The modular nature also allows for adaption to various domains and jurisdictions. 

Monitoring and Observability

This module provides the robust monitoring and evaluation mechanism to track the performance of the RAG applications, token consumption and cost

Below diagram describes the high-level architecture to build the advance level RAG application.

Agentic RAG

The Agentic-RAG refers here to employ autonomous, real-time, goal oriented and multi-step execution capabilities for business-critical applications that rely on up-to-date information. The most popular agentic framework is ReAct: Synergizing Reasoning and Acting in LLMs. The Agentic-RAG stage includes additional capabilities such as Tools to develop agents, Response Evaluator and State Management to handle single-turn or multi-turn RAG use cases.

Tools

This component employs access to both internal and external tools to make informed decision about the task. Such as incorporating tools such as calculator, web search for information, APIs to retrieve real time information, code execution specific to a use case requirement. 

Response Evaluator

This component instead of simply providing response to a query, the system autonomously determines what was the original query, what information or context it needs to respond, breaking requests into multiple steps, sending it to tools and/or LLM for the response generation, evaluate its output, refine query and improve response until satisfactory.

State Management

This component deals with the tracking of context over time to manage tasks that involve multiple iterations.

Conclusion

AI adoption in enterprises follows a spectrum—from embedding AI in existing applications to developing AI-native solutions. By leveraging RAG, organizations can significantly enhance the accuracy, efficiency, and contextual awareness of AI-powered systems, ensuring better decision-making and automation across all business functions.

For enterprises looking to scale AI adoption, the key lies in:

• Starting with AI-Embedded solutions for modernization
• Leveraging AI-Assisted applications for better human-AI collaboration 
• Building AI-Centric applications to unlock new business opportunities

With RAG-powered AI, businesses can ensure their AI applications remain relevant, reliable, and continuously updated, paving the way for smarter, more efficient enterprises of the future.

Additional Contributors

• Megan Winsby (Senior UI/UX Designer, IBM)
• Tarun Sharma (Senior Solution Architect, IBM)

References

1. Retrieval-Augmented Generation for Large Language Models: A Survey: https://arxiv.org/abs/2312.10997 
2. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks: https://arxiv.org/abs/2005.11401
3. Types of RAG Strategy: https://newsletter.armand.so/p/comprehensive-guide-rag-implementations