RAG vs. LLM fine-tuning: Which is the best approach?

As enterprises double down on AI strategy, two methods frequently come up: retrieval-augmented generation (RAG) and large language model (LLM) fine-tuning. Both are powerful ways to improve model performance, but they serve different needs — and often work best when paired together.

This post breaks down how RAG and fine-tuning work, where they shine, and how to decide which fits your enterprise use case. We’ll also explore why many companies combine both approaches to get the best of each.

What is retrieval-augmented generation (RAG)?

RAG enhances an LLM’s performance by supplementing it with enterprise data at runtime. Instead of relying solely on pre-trained knowledge, the model retrieves relevant content from internal sources — such as Google Drive, Confluence, Jira, or your own proprietary systems — and uses that context to inform its responses.

The process unfolds in two stages:

1. The system searches internal data repositories based on the user’s query.
2. The most relevant content is passed to the model along with the prompt to generate a grounded, informed response.
   

This architecture dramatically improves the accuracy, relevance, and trustworthiness of AI outputs, especially in environments where data changes frequently or is unique to the organization.

Key benefits of RAG

• Contextual relevance: Ensures outputs reflect up-to-date, organization-specific knowledge.
• Reduced hallucinations: Provides a factual grounding, reducing the chance of misleading responses.
• No model retraining required: RAG layers on top of existing LLMs, making it easier to implement and scale.

RAG use cases

RAG is ideal for knowledge-heavy workflows where recency or internal context matters:

• Customer support assistants that pull real-time product info and policy documentation
• Legal teams conducting research with the latest case law and compliance updates
• Medical analysts synthesizing recent research alongside patient history
• Domain-specific translation tools that adapt outputs based on enterprise lexicons

Because RAG doesn’t require retraining the model itself, it’s a flexible and efficient option for delivering personalized, up-to-date results across departments.

What is LLM fine-tuning?

Fine-tuning involves continuing the training of a foundation model on a domain-specific dataset. The goal is to help the model learn the nuances, language patterns, and logic structures specific to a particular industry, product, or task.

Unlike RAG, which feeds fresh content to a model at inference time, fine-tuning bakes that domain expertise into the model itself.

Key benefits of fine-tuning

• Domain fluency: Models better understand specialized terminology and workflows.
• Task precision: Fine-tuned models excel at targeted tasks like summarization, classification, or legal clause detection.
• Consistency: Responses adhere more closely to internal voice, tone, or format requirements.

Fine-tuning use cases

Fine-tuning is particularly useful when models need to perform the same task reliably at scale:

• Healthcare: Interpreting clinical notes and suggesting treatments based on guidelines
• Finance: Generating insights from earnings reports or regulatory filings
• Legal: Analyzing contract terms or identifying risk in procurement documents

Fine-tuned models perform best when the underlying task or knowledge set is relatively stable, making them ideal for structured, repeatable work in specialized domains.

When to choose RAG for your enterprise

For enterprises seeking to enhance AI capabilities with current and context-rich data, RAG provides a compelling solution. This approach is particularly beneficial in environments where the ability to swiftly access and utilize the latest information is critical. By seamlessly integrating real-time data retrieval with advanced language processing, RAG delivers outputs that are both precise and contextually aligned with user requirements.

Optimal scenarios for RAG

• Adaptability to dynamic data: RAG is ideal for industries where the information landscape changes rapidly, such as financial markets or regulatory compliance. It ensures AI systems can retrieve and utilize the most recent data, maintaining decision-making relevance and accuracy.
• Enhanced customer engagement: In customer service, RAG elevates the performance of virtual assistants by providing detailed, personalized interactions. By drawing on a multitude of data sources, these AI tools can craft responses that are finely tuned to individual customer profiles and histories.
• Complex domain applications: Sectors like healthcare and legal services, which require deep domain knowledge and up-to-date information, benefit significantly from RAG. In healthcare, RAG aids in synthesizing recent medical research with patient data, while in legal settings, it supports comprehensive document analysis with the latest legal information.

RAG shines when knowledge exists, but is scattered and you need AI to surface it quickly and accurately.

When to opt for LLM fine-tuning

For enterprises looking to tailor AI models for specific tasks or domains, LLM fine-tuning is a smart option. It’s especially useful in areas that require deep understanding of industry-specific language and context. By customizing models through fine-tuning, businesses can ensure AI outputs are tailored to the specific demands and nuances of their industry.

Strategic benefits of fine-tuning

In sectors such as healthcare, finance, and legal, fine-tuning delivers a marked edge. It enables models to master the specialized language and context unique to these domains. This mastery enhances a model's ability to perform detailed analyses and produce content that meets stringent industry criteria.

• Precision in specialized tasks: Fine-tuning allows models to excel in tasks like named-entity recognition, which is critical for processing legal documents, analyzing medical records, and generating financial reports. This precision in identifying key terms and entities facilitates informed decision-making and ensures regulatory compliance.
• Insightful sentiment analysis: In customer relations and market research, grasping sentiment and tone is crucial. Fine-tuned models adeptly perceive subtle shifts in language, offering insights that drive engagement strategies and competitive positioning.

Fine-tuning gives enterprises sharper, more tailored AI capabilities, helping models generate outputs that are both accurate and aligned with the task at hand. It’s a valuable way to scale specialized applications and make AI a strategic advantage in your day-to-day work.

If the task is repetitive, requires specific phrasing, or depends on subject-matter expertise, fine-tuning adds valuable precision.

Comparing RAG vs LLM fine-tuning

Choosing the right approach depends on your goals, your team, and your data. 

Ask yourself:

• Does my use case require up-to-date answers?
• Are my data sources structured or unstructured?
• Do I have strong domain-specific data for training?
• What technical expertise and infrastructure do we have?
  

For many teams, starting with RAG is a lower-lift way to get meaningful value from AI quickly. As your strategy matures, layering in fine-tuned models helps sharpen performance for specialized tasks.

Can you combine both RAG and LLM fine-tuning?

Yes, and in many cases, you should.

Using RAG and fine-tuning together allows you to pair deep expertise with real-time relevance. A fine-tuned model provides a strong foundation, while RAG brings in the freshest data available at the moment of the query.

Example: A legal AI assistant can be fine-tuned on historical case documents and statutes to ensure it understands legal structure and terminology. With RAG, it can then incorporate recent rulings or client-specific data to generate accurate, personalized summaries.

This hybrid setup is increasingly common across industries like:

• Customer service, where personalization and fast retrieval are key
• Healthcare, where guidelines evolve and patient data is time-sensitive
• Sales, where each interaction requires understanding both context and CRM records

Thinking beyond the model

Choosing between RAG and fine-tuning isn’t just about the model architecture. It’s about how your business thinks about knowledge, precision, and change. Some problems demand fresh, up-to-the-minute context. Others require deep fluency in a narrow domain. Many demand both.

Rather than starting with the technology, start with your problem. What does your team need to do faster, smarter, or more accurately? What kind of information matters most, and how often does it change?

The most effective AI strategies meet your business where it is today and evolve alongside it. That’s the power of combining retrieval with specialization. It’s not just about building smarter models — it’s about enabling smarter work.

Glean can help you do both

At Glean, we’ve built our Work Assistant to support both approaches. Our platform connects to over 100 tools, bringing together the knowledge your employees need. And with support for fine-tuned models and custom agents, you can build solutions tailored to your business — from real-time answers to expert-level analysis.

Request a demo to explore how enterprise AI can work for your teams.