How You Can Use RAG to Make Your AI Strategy More Cost-Efficient

Most enterprises are investing heavily in GenAI pilots, but many still struggle to deploy it at scale or create sustained business value. Why? Because the true cost of deploying AI without the right architecture can be prohibitive.

According to Gartner, organizations often abandon AI initiatives after proof of concept because of poor data quality and inadequate risk controls. In fact, Gartner predicts that through 2026, organizations will abandon 60% of AI projects that aren’t supported by high-quality, AI-ready data.

One of the main reasons AI projects fail is cost leakage hidden inside AI workflows. Oversized prompts with copied context or containing multiple documents can burn through tokens quickly. Teams also default to overly powerful (and expensive) models for use cases that don’t really require them, further inflating costs. Hallucinations can also introduce downstream costs from rework and manual validation, adding compliance risk.

However, when AI leverages trusted, governed enterprise data through a retrieval layer at the time of inquiry, it’s far more efficient and cost-effective. This process is called Retrieval-Augmented Generation (RAG), and it reduces token waste while limiting unnecessary model usage. Other benefits include reduced hallucination risk and stronger access controls, without continuous retraining.

This blog explores how RAG enables more efficient, governable AI, and why it’s now a critical requirement for scaling enterprise AI cost-effectively.

What Is Retrieval-Augmented Generation (RAG)?

RAG combines retrieval – finding relevant information from trusted sources – with generation – using large language models (LLMs) to produce a response. Rather than relying on training data to generate an answer, RAG provides answers that are grounded in data you’ve entered that is accurate with up-to-date context.

Here’s how it works:

1. A user asks a question
2. The system retrieves relevant information from ingested sources such as internal documents, knowledge bases or systems of record
3. The LLM generates an answer using that information and may even include citations source citations

In this approach, the LLM doesn’t have to guess the answer based on generalized knowledge because it’s responding based on verified, permissioned data.

In enterprise scenarios that require accuracy, traceability and governance, RAG is especially valuable. For example:

• AI based on internal company knowledge, where responses must reflect current business reality
• Enterprise AI search, when users need precise answers rather than keyword matches or summaries that could contain hallucinations
• Enterprise AI assistants that must produce explainable and auditable results

To understand why RAG matters, it helps to understand the limitations of traditional GenAI.

Why Traditional GenAI Alone Falls Short in Enterprise Use Cases

Enterprise environments have strict standards, and when AI outputs influence decisions, operations or compliance, a high level of quality and accuracy is essential.

Unfortunately, standalone LLMs usually don’t meet those standards because of several limitations:

• Cost and Operational Overhead: The recurring costs and token usage associated with complex prompts and model retraining can add up quickly as the same information is uploaded and reprocessed repeatedly.
• Static Knowledge: LLMs are trained at a single point in time, so they may not be aware of the latest pricing, supplier terms or policies.
• Access Control Gaps: Traditional GenAI often lacks the ability to impose enterprise governance standards on employees.
• Hallucinations: Traditional GenAI generates responses based on probability alone. This means some of the information may be incorrect or even fabricated.

By grounding AI outputs in governed, up-to-date data with traceable sources and built-in access controls, RAG ensures GenAI is ready for enterprise deployment.

How RAG Makes AI Strategies More Time- and Cost-Efficient

RAG works differently from traditional GenAI because it combines parametric memory (what the model was trained on) and non-parametric memory (what it can look up in real time). This combination matters because enterprise knowledge changes constantly, but models don’t.

RAG updates knowledge at the moment the question is asked without requiring model retraining, lowering costs, improving accuracy and enabling better governance.

Infrastructure and Operating Cost Savings

RAG changes the economics of AI systems. High-quality retrieval limits the amount of context sent to the LLM, so you no longer need to upload document(s) each time you ask a question. RAG will retrieve the best paragraph, sentence, document title, and timestamp from a video on a per-query basis. Since LLMs are token-based (number of words/characters), if you're limiting the amount of information sent to the LLM, you'll use the LLMs more efficiently (the LLM doesn't have to scan through the entire document every time it answers a question). Plus, since the knowledge is separate from the model itself, query prompts don’t need to be as complex.

Accuracy Without Model Retraining

When a user asks a question, the retrieval layer pulls the most authoritative content available from approved sources and provides it to the model as context. The LLM uses this retrieved data to generate the response. Because updates to stored knowledge are separate from updates to the model, you don’t need to retrain the model when contextual data changes.

Up-to-Date AI Responses

RAG retrieves information in real time, so the AI responses match the current business reality. If policies change or documents are updated, the AI will reflect those updates immediately. Eliminating the need to retrain the model reduces operational overhead while improving user trust.

Enables Explainability, Trust and Governance

With RAG, responses to AI queries have credible citations, traceable sources and logs; meaning outputs are verifiable and auditable. RAG also supports permission-aware retrieval where users only see responses based on data they are authorized to access. This reduces the risk of data leakage or noncompliance. RAG also helps reduce hallucinations by grounding responses in curated enterprise data.

Designed for Continuous Improvement

RAG solutions such as the Progress^® Agentic RAG platform support continuous improvement through built-in evaluation and auditing. RAG Evaluation Metrics (REMi) assess the quality, relevance and verifiability of the generated answer. The audit capabilities reveal who is asking what and whether answers may need to be refined. It’s a feedback loop designed to support AI performance improvements over time.

Grounding Agentic AI in Verifiable Data

In enterprise AI, RAG and AI agents work together. AI agents are designed to act – they apply rules and carry out tasks across systems. RAG acts as the knowledge foundation that gives the agents reliable data to fuel their decisions and actions.

Grounding the decisions and actions of AI agents in verified knowledge is essential for governance and risk control. When agents lack reliable data, they can easily hallucinate or leverage unauthorized data, impact traceability and explainability.

However, when AI agents work with RAG, users can leverage AI to complete complex tasks with far less risk. For example, a user may provide the prompt: “Summarize supplier risks and open tickets, then draft a mitigation plan.” In response, RAG will retrieve the relevant data to help the AI agent decide what actions to take, with logging and approvals built in.

Now let’s explore some best practices for implementing cost-efficient RAG in your organization.

5 Best Practices for Scaling RAG

Implementing RAG should be approached as foundational to your infrastructure, rather than a standalone feature. Here are five recommended best practices:

1. Start with high-impact, low-risk use cases. Begin with clearly bounded scenarios like policy Q&A, contract clause lookups or internal enablement. These use cases make it easy to test accuracy, citations and user trust, and they deliver quick wins without overcomplicating the system.
   
   
2. Ensure data accuracy. RAG is only as good as the data it retrieves. Invest upfront in clean content, strong metadata and a clear refresh cadence, so that information stays current and easy to rank.
   
   
3. Think modular. Design retrieval pipelines as modular components that can support multiple assistants and workflows, rather than rebuilding them each time. This reduces duplication, improves consistency and makes it easier to scale across teams.
   
   
4. Pay attention to retrieval quality. Don’t judge success by how polished the answer sounds. Track what matters: precision and recall, citation accuracy and source relevance. Relevant retrieval is what keeps answers grounded and trustworthy.
   
   
5. Test it out first. Agents need accurate, trusted information to work from and take action, so be sure your retrieval layer is working reliably before you deploy agents.

These best practices will help you get on started on the right foot when it comes to RAG, but you should always try to avoid these common pitfalls:

• Don’t treat RAG like simple search plus AI without tuning what gets retrieved and ranked
  
  
• Don’t feed RAG ungoverned or low-quality content and expect trustworthy results
  
  
• Don’t overload the model with too much context, which lowers answer quality and drives up costs
  
  
• Don’t skip human review for high-impact use cases, where mistakes carry real risk

As TechCrunch cautions, RAG is not a silver bullet. Without strong retrieval quality, governance and validation, it can still produce unreliable or risky outputs. Avoiding these pitfalls ensures RAG delivers on the promise of grounded, explainable and scalable enterprise AI.

Better Data, Efficient AI

When implemented with careful planning and execution, RAG fixes the problems that cause most AI initiatives to fail: high costs, unreliable answers and weak governance. By grounding AI in trusted, up-to-date enterprise data, RAG reduces waste and limits risk, making AI systems easier to manage as they scale.

The goal isn’t to deploy more AI tools, but to strive for efficiency with confidence and cost discipline. Instead of constant retraining, rework or manual oversight, the goal is AI that behaves predictably, scales efficiently and becomes a dependable part of your infrastructure, delivering long-term strategic value.