RAG vs. LLM: When to use retrieval, fine-tuning, or both

A large language model (LLM) generates language from its training data, while retrieval-augmented generation (RAG) pulls relevant company content into the LLM’s prompt at query time. So you don’t choose one over the other. Use RAG on top of an LLM when answers depend on private, frequently changing, or auditable information.

LLM fine-tuning is a third option. Fine-tuning continues training a model on domain-specific data so it learns your terminology, tone, and formats. For examples of RAG at work, see our top 10 enterprise use cases for RAG models.

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 compare RAG with a standalone LLM, cover RAG’s limitations, and explore why many companies combine both approaches to get the best of each.

What’s the difference between RAG and an LLM?

An LLM generates language from patterns learned during training, so its knowledge stops at a fixed cutoff. RAG adds a retrieval step that pulls current, company-specific sources into the prompt before the model answers, which makes citations possible. RAG still needs an LLM to write the final response.

• Knowledge source: An LLM draws on its training data. RAG draws on documents retrieved at query time.
• Freshness: An LLM’s knowledge is fixed at its training cutoff. RAG reflects sources as soon as they’re updated and indexed.
• Private data: An LLM has never seen your internal docs. RAG can search connected tools like Confluence, Jira, and Google Drive.
• Source citations: An LLM can’t point to where an answer came from. RAG can link each answer to the documents it used.

A plain LLM can’t cite a specific document because it doesn’t store documents. Training compresses text into statistical patterns, so the model can repeat a policy’s wording without knowing which file it came from. Retrieval keeps the link between an answer and its source.

Glean Assistant uses that link to return cited answers grounded in your company’s knowledge. You can open the source document and check the answer before you act on it.

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 three steps:

1. Retrieve: The system searches connected data sources based on the user’s query.
2. Augment: The most relevant passages are passed to the model along with the prompt.
3. Generate: The LLM writes a response grounded in that context, with citations to the sources it used.

In an enterprise, RAG retrieval also has to respect each user’s existing access. Without that check, the LLM can surface content the user isn’t allowed to see, like a salary spreadsheet or an unreleased roadmap. Glean mirrors source permissions and enforces them at retrieval, upstream of the LLM, so answers draw on content the user is authorized to access.

This architecture 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: Keeps outputs current with 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 perform well on 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

Choose RAG instead of a plain LLM when answers must come from private, frequently changing, or verifiable sources like policies, product docs, support tickets, or runbooks. A plain LLM can’t see those sources, and it can’t show where an answer came from. RAG retrieves the latest information at query time, so outputs stay precise and match what the user needs.

Optimal scenarios for RAG

• Adaptability to dynamic data: RAG is ideal for industries where information changes rapidly, such as financial markets or regulatory compliance. It lets AI systems retrieve and use 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 thorough 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 tailor AI outputs 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 perform well 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 supports informed decisions and regulatory compliance.
• Insightful sentiment analysis: In customer relations and market research, grasping sentiment and tone is crucial. Fine-tuned models pick up 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, a standalone LLM, and fine-tuning

Standalone LLMRAGFine-tuningData freshnessFixed at training cutoffHigh: pulls in real-time enterprise dataLow: fixed after trainingSetup effortLow: works out of the boxMedium: requires data connectors and orchestrationHigh: requires ML expertise and retrainingTask performanceGeneral-purpose, limited to training dataGeneral-purpose with context injectionHighly specific and optimized for accuracyScalabilityFast to deploy, but knowledge updates wait for new model versionsFlexible and fast to updateMore compute-intensive and time-consumingIdeal forGeneral writing, brainstorming, summarizing provided textKnowledge retrieval, assistants, real-time answersClassification, summarization, structured outputSource citationsNoYes: links answers to source documentsNoRespects permissionsNo: has no view of company access controlsYes, when retrieval is permission-awareNo: training data has no per-user access controls

Choosing the right approach in the RAG vs. LLM decision 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.

What are the limitations of RAG?

RAG is only as good as what it retrieves. If the search step returns outdated, conflicting, or restricted content, the LLM builds its answer on that content. RAG reduces hallucinations but does not eliminate them, so teams still need source maintenance, permission enforcement, and a way to measure answer quality.

• Outdated or conflicting sources: If three versions of a pricing doc exist, retrieval can pass the wrong one to the model.
• Weak ranking for short content: Chat messages are short and often untitled, which gives text-based ranking less to work with.
• Permission leaks: If retrieval doesn’t enforce access controls, the LLM can expose content the user isn’t allowed to see.
• Remaining hallucinations: RAG lowers the risk of made-up answers, but the model can still misread retrieved context.

Measuring answer quality is part of running RAG well. Glean grades both retrieval and generation. In a 2024 internal evaluation, Glean’s AI evaluator agreed with human raters’ judgments 74% of the time, while Glean team members rating the same responses agreed with each other 79% of the time.

Glean’s hybrid search ranks content with semantic matching plus signals like recency and authority, and the Enterprise Graph adds relationships among people, content, and activity. That gives retrieval more signals to work with than text similarity alone.

Can you combine both RAG and LLM fine-tuning?

Yes, and in many cases, you should.

Combining RAG and fine-tuning 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 so it understands legal structure and terminology. With RAG, it can then incorporate recent rulings or client-specific data to generate accurate, personalized summaries.

Teams can combine the two. Examples where a hybrid approach can help:

• 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

Glean’s Model Hub gives teams access to more than 40 open and proprietary LLMs, and retrieval grounds those models in company knowledge.

Thinking beyond the model

Choosing between RAG and fine-tuning depends less on model architecture than on 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. Smarter models matter most when they lead to smarter work.

Glean can help you do both

At Glean, we’ve built Glean Assistant to ground answers in company knowledge. Our platform offers more than 275 native and MCP-based connectors to the tools your teams use, bringing together the knowledge your employees need. And with agent orchestration, you can connect specialized agents to complete multi-step tasks for your business.

Glean combines hybrid search and the Enterprise Graph with RAG. Hybrid search finds relevant content across connected tools, and the Enterprise Graph adds context about how people, documents, and activity relate. Glean mirrors source permissions and enforces them at retrieval, upstream of the LLM, so cited answers draw on content the user is authorized to access.

Glean Agents can plan and run multi-step work, like triaging a support ticket, with enterprise context and governance.

Request a demo to explore how Glean and AI can transform your workplace.

Frequently asked questions

Can RAG work without an LLM?

No. Retrieval alone returns a list of documents, and the LLM is what reads them and writes the answer.

Does RAG eliminate hallucinations?

No. RAG reduces hallucinations by grounding answers in retrieved sources, but answers are still only as accurate as those sources and the retrieval step that finds them.

When should you choose fine-tuning over RAG?

Choose fine-tuning when the task is stable and repetitive and depends on format, tone, or domain fluency rather than fresh facts. Legal clause detection and ticket classification are common examples.

Can an LLM access enterprise data without RAG?

Not in a way that scales. You can paste data into prompts or retrain the model, but neither keeps up with data that changes often and carries different permissions for each user.

Is RAG more expensive than a standalone LLM?

RAG adds retrieval infrastructure to maintain, like connectors, indexing, and search. Updating its knowledge means refreshing the index rather than retraining the model.

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