What Is RAG in AI? Understanding the Relationship Between AI and RAG

You’re trying to answer a simple question (“What’s our approved policy for vendor risk reviews?”), but the answer is buried among Confluence pages, old Slack threads, and a FAQs sheet that may or may not be up to date. 

That uncertainty slows you down and creates risk for your team.

This is the challenge with large language models (LLMs) in the enterprise: they’re powerful, but they often rely on training data unless augmented with external tools or retrieval systems, leading to outdated or incomplete answers. And when AI gets it wrong, the impact can mean compliance issues or unintended data exposure. 

As generative AI becomes embedded in everyday workflows, the need for accurate, up-to-date, and verifiable responses becomes critical.

Retrieval-augmented generation (RAG) addresses this issue by retrieving and injecting relevant enterprise data into the model’s prompt at query time, without retraining models, while still supporting secure access to proprietary data. 

It’s a foundational shift for how AI can support work across finance, legal, operations, IT, and customer teams. 

Let’s explore RAG in more detail, including how it works and its use cases, but keep in mind that retrieval alone doesn’t solve broader limitations like retrieval accuracy, ranking, or reasoning gaps that many organizations are running into. 

Enterprises increasingly need AI systems that can retrieve trusted information, reason over context, and support secure actions across systems. 

What is retrieval-augmented generation (RAG) in AI?

RAG is an AI architecture (or design pattern) that helps improve LLM accuracy by retrieving relevant data from trusted knowledge bases before generating a response. 

That data is then injected into the model’s prompt as context. This allows the system to access external, non-parametric knowledge beyond what is stored in the model’s training data. 

First introduced in a 2020 paper by Patrick Lewis and colleagues, RAG was designed to solve a simple but important gap: LLMs don’t inherently know what’s happening inside your organization. 

• LLMs can primarily rely on their training data unless augmented with external tools or retrieval systems, which may not reflect current policies or systems.
• They don’t have access to proprietary organizational knowledge by default.
• They can generate responses that sound correct but are actually outdated or out of context.

Think of it like giving an AI assistant access to your company’s reference library with indexed enterprise data it can retrieve at query time to formulate its response, rather than relying on what it learned during training. 

When you ask a question, RAG encodes it into an embedding (a numerical representation of meaning) and performs a semantic similarity search often using a vector or hybrid index, to find relevant information. 

The system pulls context from connected systems like knowledge bases, HR platforms, IT service tools, and document repositories. This enables the system to find relevant information even when the wording doesn’t exactly match the query, unlike traditional keyword search.

The LLM then uses that grounded info to generate responses, constrained by the model’s context window and the quality of retrieved results. 

The effectiveness of RAG depends heavily on retrieval quality, including how data is chunked, indexed, ranked, and filtered before being passed to the model.

Why enterprises care about RAG

For enterprises, the challenge is whether you can trust what AI says, not whether it can respond. 

Imagine an employee asking, “What’s the company’s PTO policy?” That info might live across an HRIS system, employee handbook, and shared drive. 

Traditional search tools may return links, leaving you to piece together the answer yourself, while AI systems without RAG risk surfacing outdated or conflicting information. 

This matters even more as enterprises face increasing scrutiny around AI governance, auditability, and control. In cases like the Air Canada chatbot incident, incorrect AI-generated guidance created legal and reputational risk, showing how quickly poor info grounding can escalate into compliance and user trust issues. 

RAG provides a foundation for trusted answers by grounding responses in retrieval-based context, without embedding sensitive details into model training. 

Because enterprise data is accessed dynamically rather than stored in model weights, it can be updated, revoked, or governed independently of the model itself.

In fact, the Gartner 2024 Generative AI Hype Cycle highlights retrieval-augmented approaches as a key capability for making gen AI more reliable in enterprise environments. 

The technology helps turn fragmented enterprise knowledge into answers you can rely on, instead of requiring you to manually search, interpret, and verify information across systems. This reduces context switching and support overhead, enabling more consistent self-service across IT and HR workflows.

It also creates the foundation for more advanced AI systems that move beyond answers to execution, supporting end-to-end workflows across enterprise systems.

How a RAG pipeline works step by step

It’s helpful to understand how RAG processes queries in a multi-step pipeline that combines retrieval and generation, enabling conversational AI experiences across enterprise systems. It might feel complex, but behind the scenes, it follows a structured pipeline that includes preprocessing, retrieval, augmentation, and generation. 

0. Preprocessing and indexing (often overlooked but critical)

Before any query is processed, enterprise data is prepared for retrieval. 

Documents from systems like knowledge bases, HR platforms, and IT tools are split into smaller chunks, converted into embeddings, and stored in a vector or hybrid index. 

This step determines how effectively relevant information can be retrieved later.

1. Query embedding and retrieval 

When you ask a question, the system translates your query into a semantic representation (embedding) that captures the meaning behind it. This allows the AI to understand intent and context, not just match exact keywords. 

From there, it searches a vector or hybrid search index (combining semantic and keyword signals) filled with pre-indexed enterprise content, including document repositories, HR platforms, IT systems, and internal knowledge bases. 

Rather than scanning for keyword matches, it uses semantic search to find information that’s conceptually similar to your query. 

So if you ask “How do I reset my VPN access?”, the system can still surface the right guidance, even if the source content is labeled “Remote access troubleshooting steps.”

In advanced implementations, retrieved results are often re-ranked using additional models or scoring mechanisms to improve relevance before being passed to the LLM. 

Hybrid approaches may also combine vector similarity with keyword-based methods (such as BM25) for better accuracy.

2. Prompt augmentation with sourced data 

Once the system retrieves relevant content chunks, it selects, filters, and injects the most relevant context into the model’s prompt, often constrained by the model’s context window and ranking logic. 

Instead of relying on what it learned during training, the AI model now has access to current, authoritative enterprise data as it prepares its response. 

Injecting trusted context directly into the prompt means the system’s response reflects real policies, procedures, and system data, not assumptions or outdated training data. 

For example, if an employee asks about parental leave eligibility, the system can retrieve the relevant HR policy details and include them in the prompt so the response aligns with the company’s actual guidelines. 

This step is key to reducing hallucinations, when retrieval and ranking are accurate, since relevant context helps guide the model before it generates an answer. 

3. Generation and source citation 

Once the prompt is enriched with retrieved context, the LLM combines that info with its own natural language understanding to generate a response. That gives you an answer that’s both natural and grounded in enterprise data, instead of based purely on training knowledge. 

In well-designed RAG systems, the response can also include references to external sources where the info came from. This isn’t automatic in every setup, but it’s a best practice that helps employees and IT teams verify answers and build trust in the output. 

In other words, an employee asking about expense reimbursement limits might receive a response that cites the relevant finance policy document, making it easy to confirm details before taking action.

This added transparency helps ensure responses are usable in real workflows, where accuracy and context matter just as much as speed. 

At this stage, the system produces a context-aware response, but additional orchestration layers are required to move from answers to actions across enterprise workflows.  

Enterprise use cases that benefit most from RAG

For many enterprises, friction shows up in the everyday moments when employees just need a clear answer, but have to dig through multiple systems to find it. 

That’s why some of the most impactful applications for RAG include routine workflows like:

IT self-service search 

Employees trying to solve a simple IT issue (resetting access, fixing a device) often end up bouncing between service desks, internal wikis, and troubleshooting guides just to find a clear answer. Hours are spent searching for info before anything gets resolved. 

RAG-based systems can surface the most relevant resolution steps directly within the flow of work, without requiring users to navigate multiple tools or knowledge sources. 

This shortens resolution time and reduces reliance on service desks for repetitive requests.

Access and permissions requests 

Getting access to tools and systems is something nearly every employee runs into, but it’s not always straightforward. While they might know what they need, the challenge is figuring out if they’re eligible, which form to use, and who has to approve it. 

RAG can consolidate eligibility rules, policies, and request steps into a single, context-aware response. 

So employees can quickly see what they’re allowed to access and the exact steps to request it. 

This reduces back-and-forth communication and helps standardize how access requests are initiated.

As a result, requests can move forward with fewer delays and less manual intervention.

Knowledge delivery in collaboration tools 

Many employees don’t really think in terms of “systems” anymore. They just want answers in the flow of work. That usually means asking in Slack, Microsoft Teams, or whatever chat tool they already have open, rather than stopping to search a portal or documentation. 

RAG enables assistants to retrieve and surface relevant knowledge directly within collaboration tools like Slack or Microsoft Teams. 

Users can just ask a question and get a helpful, context-aware response right where work is already happening. 

It also scales in a way traditional support can’t. Whether you’re supporting 1,000 or 100,00 employees, RAG helps deliver consistent, personalized answers without needing to expand headcount every time demand grows. 

Finance and procurement workflows

Finance and procurement questions tend to pop up right when people are trying to get work done, like submitting an expense or requesting a purchase. But the answer usually isn’t in one place. Policies might live in one system, while approval steps are in another, and exceptions are somewhere else entirely. 

RAG can consolidate policy details, approval steps, and exceptions into a unified response. Instead of switching between systems or waiting on clarification, employees can easily understand what’s required and what the next step should be. 

That clarity keeps things moving, so requests aren’t stuck in limbo. There’s also less back-and-forth with finance teams, and approvals can happen with fewer delays for better operational efficiency. 

Legal and compliance knowledge access

During high-stakes moments like contract reviews, legal and compliance questions need fast answers. Unfortunately, those answers are often buried in long policy docs or spread across systems. 

Rather than scanning entire documents or escalating to legal for clarification, employees can use retrieval-based systems to surface the exact clauses, policies, or compliance guidance that apply to the situation.

With context-aware answers at their fingertips, teams can spend less time second-guessing interpretations or escalating requests, keeping work moving without unnecessary compliance risk. 

Enterprise search across systems 

Enterprise search spanning apps, knowledge bases, and internal systems is where RAG has one of its biggest impacts. It transforms how employees access information across enterprise environments, empowering them to ask a question and get a clear, direct response, not a list of links. 

In well-designed systems, responses are grounded in real enterprise data and include citations or references so employees can verify information when needed. This helps reduce ambiguity and makes it easier to trust what’s being surfaced. 

But keep in mind that enterprise search also needs to respect permissions. Granular access controls ensure employees only see what they’re authorized to, keeping security and compliance intact even as information becomes easier to find. 

At scale, this shifts enterprise search from a retrieval experience to a more unified, answer-oriented access layer across the organization. 

Governance and security considerations for RAG systems

For enterprise leaders, RAG has to meet the same security, governance, and compliance bar as any other system handling sensitive data. That typically comes down to two core areas: access control and auditability. 

RAG does not inherently enforce security or governance. 

These controls must be explicitly implemented across data sources, retrieval layers, and model interactions to ensure outputs remain aligned with enterprise policies.

Data isolation and permission checks 

In an enterprise RAG setup, employees should only see info they’re already authorized to access, even when AI pulls from dozens of connected systems.

That’s handled through identity-aware and permission-aware retrieval mechanisms. Before the system returns any results, it should enforce access controls from underlying systems to verify what the user is entitled to see and filter everything else out. 

In practice, enforcing consistent permissions across multiple systems can be challenging, particularly when access models differ between platforms.

When this isn’t done well, sensitive or restricted info can slip into responses where it doesn’t belong. That’s why the governance layer matters just as much as the model itself. 

These safeguards should apply across document repositories, HR platforms, financial systems, and other internal tools, with encryption and data governance policies in place to keep data protected as it moves across systems.  

RAG systems must also account for prompt injection and data leakage risks. 

Retrieved content can contain hidden or malicious instructions that attempt to influence model behavior, making it important to validate, filter, and constrain what is passed into the model’s prompt. 

Without these safeguards, sensitive data could be exposed or responses could be manipulated.

Additional validation and filtering steps may introduce latency, requiring careful balancing between response speed and security.

Audit trails and compliance mapping 

Audit trails show where answers are coming from: what the employee asked, which sources the AI retrieved, and how it generated the response. If something looks incorrect or unexpected, IT and security teams can go back and see exactly which document, policy, or data source influenced the response. 

For regulated industries, audit logs provide the documentation needed to show how AI sourced and handled information, which is often required during reviews or compliance checks. 

These controls can help reduce risk, but do not eliminate it entirely, especially in complex or highly dynamic environments.

From accurate answers to automated actions with agentic AI

RAG can improve the quality of answers by grounding responses in retrieved enterprise context, but it still stops at “here’s what you need to know.” Agentic AI can build on that foundation, moving from answers to executable actions. 

Think of it as two layers working together: 

• A RAG-powered knowledge layer that provides relevant, contextual information
• A reasoning and orchestration layer that determines next steps and executes tasks across systems 

If an employee requests a password reset, the system first uses RAG to retrieve the relevant policy and requirements. From there, an AI agent can verify identity, trigger the reset in the identity management system, and confirm once it’s completed. 

This is the direction many enterprises are moving toward — systems that not only provide information, but also help complete tasks across enterprise workflows.

Start exploring Agentic RAG in your environment today 

RAG retrieves relevant enterprise information and uses it to generate concise, grounded answers. 

Agentic RAG goes further — adding reasoning to interpret intent, prioritize the right systems and content, and handle complex questions. 

The result: more reliable, context-aware answers with verifiable references, so employees don’t have to search across tools.

Moveworks applies this approach to enterprise search by combining retrieval with goal-oriented reasoning, so the system can enrich queries, plan an approach across sources, and return concise answers with references. 

By incorporating signals such as source authority/recency, permissions, and activity signals (e.g., most viewed or highly rated), it helps surface information employees are more likely to trust and act on.

This shifts the experience from search to action, helping employees not only find information, but move work forward by guiding next steps or triggering workflows across IT, HR, or finance systems, with less tool switching.

Explore Moveworks AI Assistant to see how agentic RAG can support search, reasoning, and action across your enterprise.