The emergence of retrieval-augmented generation (RAG) has revealed new possibilities in artificial intelligence by combining retrieval mechanisms with generative models to produce more accurate and contextually aware responses.

To successfully implement an enterprise-level RAG system, you need to start with comprehensive scoping. This ensures that your RAG system not only delivers trusted accuracy but also scales to meet enterprise demands, complies with stringent security and data governance standards, and delivers measurable value.

This article outlines the key steps necessary for scoping a RAG implementation and features two downloadable templates to help you embark on your enterprise RAG journey.

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Selecting and Prioritizing Use Cases for RAG

Ready to learn how to implement retrieval-augmented generation? The first step in planning a RAG implementation is to identify and prioritize potential use cases. RAG systems can support a wide range of business processes for internal stakeholders, such as sales, customer service, marketing, and R&D, as well as external stakeholders, including your customers, partners, and suppliers.

Focus on opportunities where improved access to and interaction with enterprise content can significantly enhance productivity, efficiency, and user experience. Pay special attention to complex, repetitive tasks that are essential to your workflows yet tedious to execute. Examples of such use cases include customer service automation, complex query answering, and content-intensive workflows like equipment maintenance.

Use cases can be prioritized along two dimensions:
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1. Potential use case value

Assess whether the RAG use case you are developing will achieve any of the following objectives:

• Address a strategic priority for your business.
• Have a material impact on employee or customer experience, cost savings, and/or revenue growth.
• Deliver substantial productivity and efficiency improvements, effectively saving time and resources.
• Enable future applications by establishing a strong foundational user experience. For example, a self-service chatbot that achieves high customer adoption can be further leveraged to gather customer feedback, gauge reactions to new product concepts, and gain other valuable insights.
  
  The greater the number of objectives a use case can achieve, the higher its value will be.

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2. Ease of Implementation

Evaluate the simplicity or complexity of integrating your RAG system by considering the following questions in relation to your organization:

• Do you have existing content that can effectively address questions relevant to your use case?
• Is your content available in industry-standard formats rather than custom file types?
• Is your content well-organized in one central system rather than stored in various source systems proliferated throughout the company?
• Can the content be accessed with read-only permissions from the source repository(ies)?
• Are business leaders and subject matter experts (SMEs) on hand to provide context, add additional content as necessary, and facilitate the training and adoption process?
• Is it easy to solicit feedback on response quality and usability from end users?
• Are IT personnel available to confirm compatibility with technology and security requirements?

The more often you respond "Yes" to a specific use case, the easier its implementation will be. For instance, if you’ve decided to start with a customer support chatbot, you likely know where most of this knowledge is stored and how to build a chatbot that meets your customers’ needs.

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RAG Use Case Prioritization Framework (With Editable Template)

To prioritize your RAG use cases, start by assessing the potential value and the ease of implementation for each one. Use the prompts provided above as a guiding framework for your assessment.

A simple matrix can help you visualize your AI use case prioritization (download yours here). Place the potential value of a RAG use case on one axis and the ease of implementation on the other. From there, plan your implementation strategy, focusing first on use cases that offer high value and high ease of implementation.

It might look something like this.

In this example, the RAG use cases we should prioritize are a sales support chatbot and a customer self-service tool, as both are high-value options that are easy to implement.
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DOWNLOAD NOW
AI Use Case Prioritization Framework Template

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8 Steps to Scoping a RAG Use Case (With Editable Template)

Once you've evaluated and prioritized your RAG use cases, you can work on defining the scope of the ones you’ve identified as top priority. Follow these eight steps to effectively scope a RAG use case (or follow along with our downloadable checklist template):
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1. Identify users and their needs

• Identify and profile your primary users, which may include internal employees, customers, or partners.
• Determine the total number of users, paying special attention to the expected number of concurrent users and any anticipated peak usage periods.
• Identify subject matter experts who can pinpoint common questions, conduct quality checks on the RAG system’s responses, and ensure ongoing monitoring of its performance.
• Designate administrators responsible for managing user access and maintaining content libraries.
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A clear understanding of your RAG system's users is crucial for informing design decisions and tailoring the user experience to meet their specific needs.

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2. Assess the scope of content in your knowledge base

• Define the corpus of content accessible to the RAG system to create your knowledge library. This may include internal documents like technical manuals, training materials, and databases, as well as external resources such as websites and public datasets.
• Ensure the content is relevant, up-to-date, and comprehensive enough to support the intended use cases.
• Identify the file formats involved (e.g., text, HTML, PDF, videos) to determine the necessary multimodal capabilities of the ingestion and retrieval systems.
• Identify the repositories in which this content exists and will continue to be maintained in. The RAG system will need connectors to each repository.
• Assess the volume of content to be ingested and maintained in the knowledge library, considering both the number of documents and their individual sizes.
• Estimate the frequency of content updates. This will influence the continuous ingestion capabilities needed for the RAG system.

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3. Scope query types and complexity

Define the types of queries the RAG system will manage. This includes:

• Categorizing anticipated questions and interactions.
• Assessing the complexity of queries.
• Identifying any domain-specific requirements.
• Proactively plugging any gaps in content that may pop up once the RAG application is deployed.
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Identifying typical query types and levels of complexity will help you design the retrieval and generative components of your RAG system.

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4. Identify access points for the RAG system  

Identify the platforms and environment from which users will access the RAG system, such as:

• Web interfaces
• Mobile apps
• Digital avatars
• Integration with existing enterprise systems
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Knowing where and how users will access your RAG system is important for designing user experiences so you can ensure great interactions across various platforms.

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5. Establish security and access control requirements

• Assess where user access controls are managed and how to integrate them into the RAG system. For example, can the RAG system access content from the repository with read-only permissions and ensure users don’t get access to documents they shouldn’t be able to see?
• Identify sensitive data and implement protective measures. Ensure compliance with relevant regulations such as GDPR or HIPAA, based on the data involved.
• Define the necessary security requirements for production deployments.
• Evaluate whether the RAG system will integrate with external chatbot services, such as Google Dialogflow or Microsoft Azure Bot, or an internal service.

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6. Identify system guardrails

Consider the following potential guardrail capabilities for your RAG system:

• Set limits on response types to define acceptable content generation outputs.
• Filter inappropriate content to ensure a safe user experience.
• Establish fallback mechanisms for handling unsupported queries.
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Implementing guardrails ensures the RAG system operates within acceptable boundaries. Guardrails protect against misuse, minimize the scope for hallucinations or biases, and help maintain system reliability and trustworthiness.

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7. Size RAG infrastructure

• Assess the computational resources required for both retrieval and generative components.
• Consider key factors such as data size, query volume, and latency requirements.
• Explore cloud-based solutions for flexibility and scalability in resource management, allowing you to adjust resources based on demand.
• Ensure the infrastructure can handle peak loads and accommodate future growth.
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Accurately sizing your RAG infrastructure is crucial for performance and scalability.

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8. Identify success criteria and change management requirements

• Establish clear evaluation metrics and success criteria to measure the performance and impact of your RAG system. Metrics might include accuracy, response time, user satisfaction, and business KPIs such as cost savings and revenue growth.
• Identify training requirements for users of the RAG system.
• Establish baselines of current performance to evaluate the economic value of the RAG system.
• Regularly review metrics to track progress and adjust strategies based on data-driven insights.

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DOWNLOAD NOW
Ultimate Checklist for Scoping an AI Use Case

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Typical RAG Implementation Project Phases

Implementing a RAG system generally consists of six key phases:

1. Discovery and Planning: Define objectives, scope, and requirements. Develop a project plan with timelines and milestones.
2. Data Preparation: Collect, clean, and organize your knowledge library. Implement data governance practices.
3. System Design: Design the RAG architecture, including retrieval mechanisms, generative models, and integration points.
4. Development and Testing: Build the system components and conduct thorough testing to ensure optimal functionality and performance.
5. Deployment and Integration: Deploy the system in the target environment and integrate with existing systems.
6. Monitoring and Optimization: Continuously monitor the system, collect feedback, and make improvements to enhance performance and the user experience.

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RAG Implementation Timelines

Building a RAG system from the ground up requires you to complete all of the steps above, with AI implementation timelines potentially stretching to 6-9 months.

Using a pre-built RAG platform like Pryon RAG Suite allows you to bypass several time-consuming steps, significantly shortening the implementation timeline to just 2-6 weeks.

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Estimating Project Resources and Budget

Accurately estimating resources and budget is essential for effective project planning and securing approval. When calculating the costs associated with your RAG implementation, consider the following factors:

• Personnel: Include data scientists, engineers, project managers, and domain experts. Consider both internal staff and external consultants if needed.
• Technology: Account for infrastructure costs, software licenses, and tools required for development, testing, and deployment.
• Training and Support: Budget for user training, system maintenance, and ongoing support.
• Contingency: Include a buffer for unexpected costs and challenges, such as performance tuning, model maintenance, and integration complexity.

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Develop a detailed budget that covers all aspects of the project, from initial development to long-term maintenance and support.

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Choosing a pre-built RAG platform like Pryon RAG Suite can significantly lower startup and scaling costs compared to building your own RAG system from scratch. This allows for more effective budgeting of your RAG deployments and leads to a greater return on investment.

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Let's recap

Scoping a RAG implementation is a complex but rewarding endeavor. By carefully selecting and prioritizing use cases, defining detailed requirements, sizing infrastructure appropriately, and planning project phases and resources, you can start leveraging AI systems to unlock new capabilities and drive significant value.

As a high-level recap, here is a step-by-step overview of how to implement RAG:

1. Select and prioritize use cases based on value and ease of implementation
2. Identify users and their needs
3. Assess the scope of content in your knowledge library
4. Scope query types and complexity  
5. Identify access points for the RAG system
6. Establish security and access control requirements
7. Identify system guardrails
8. Size RAG infrastructure  
9. Identify success criteria and change management requirements  

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With thorough planning and execution, RAG implementations can transform how internal and external users interact with your unstructured data, reducing knowledge friction between creators and consumers of enterprise content.

Want to learn more about what it takes to build and scale RAG in your enterprise? Download our comprehensive guide: How to Get Enterprise RAG Right.

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Ready to fast-track your RAG implementation?

Reach out to our team to discover how Pryon RAG Suite provides best-in-class ingestion, retrieval, and generative capabilities for building and scaling an enterprise RAG architecture.

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