Fine-tuning, RAG, or Prompting: Which Method to Choose to Customize AI in Your Company

Sooner or later, any company that starts using AI seriously asks the same question: "My assistant isn't responding how I want; how do I fix it?" And three possible paths appear, with names that sound similar but do very different things: prompting, RAG, and fine-tuning.

The decision is not trivial. Choosing incorrectly means spending weeks training a model when a good prompt was enough, or setting up a document retrieval system when the problem was something else. Even worse: combining them without criteria can produce expensive, slow systems that still fail to solve what you wanted to solve.

In our previous article, we thoroughly explained the benefits of fine-tuning LLMs for companies. This article complements that with the practical question: when is each approach appropriate, how to decide, and how to save yourself the long road.

The Three Levers: What Does Each One Do?

Before deciding, you must understand that they are not interchangeable alternatives. They are different levers that act on different dimensions of the problem.

Prompting: Adjusting Behavior Through Instructions

This is the most basic: writing better instructions for the model in every conversation. You explain the context, the tone, the format, what it should and should not do. The model doesn't change; what changes is what you ask it to do.

We cover the principles in our prompting guide for business owners. It is not a minor technique: a well-written prompt solves a surprisingly large part of initial problems.

RAG: Connecting with Real Information

RAG (Retrieval-Augmented Generation) allows the AI to consult your documents in real-time before responding. It does not modify the model; it gives it access to information it otherwise wouldn't know.

Think of an employee with access to an internal search engine. When you ask it something specific, it first consults and then answers.

Fine-tuning: Training a Custom Model

Fine-tuning changes the model itself. You give it thousands of proprietary examples, and the model adjusts its parameters to replicate the pattern. The behavior becomes internalized: it doesn't depend on instructions or document searches; it depends on the fact that the model is now different.

It is the most powerful lever, but also the most costly in terms of time and resources.

The General Rule: From Simplest to Most Complex

The practical recommendation for any starting SME:

Start with prompting. Move to RAG if you need real company knowledge. Only consider fine-tuning when the previous two reach their limits.

Each step is more expensive and slower than the previous one. Jumping directly to fine-tuning without exhausting prompting is, in most cases, a waste of resources.

When Prompting Is Enough

There is a set of problems that are solved well with clear instructions alone. You don't need anything else:

• Tone and format: "always respond in Spanish, friendly tone, structure in three paragraphs"
• Simple rules: "if the client asks for a quote, refer to the sales department; do not promise deadlines"
• Text transformations: summarizing, translating, rephrasing, classifying with obvious categories
• Creative generation with guidelines: marketing texts, drafts, ideas
• Specific or low-volume tasks: if you use it 5 times a day, you don't need more

Sign that prompting is sufficient: you achieve satisfactory results by repeating the same instructions and only varying the specific query. If the problem disappears when you refine the prompt, you do not need RAG or fine-tuning.

When You Need RAG

Prompting falls short when the problem is that the model doesn't know something it should know. No matter how well you write the instructions: if the model doesn't know your catalog, your return policy, or your customer history, it cannot answer well.

These are clear symptoms that you need RAG:

• The model invents concrete data (prices, deadlines, specifications) when you ask it
• You need it to respond with changing information: stock, availability, rates, news
• You have a lot of internal documentation that is impossible to put into a prompt (manuals, policies, technical sheets)
• Your team wastes time searching for information in shared folders or wikis
• You want the AI to respond about historical cases (resolved tickets, previous projects, past decisions)

RAG is the appropriate tool for these cases because it separates two things: reasoning (which the model does) and data (which comes from your documentation). If a policy changes tomorrow, you update the document, and the system already responds with the new information. Without retraining anything.

When You Need Fine-tuning

There is a set of problems where neither prompting nor RAG reaches. This is the domain of fine-tuning. It appears when the problem is not what the model knows, but how it does it:

• You need absolute consistency in tone and style, and long prompts don't achieve it completely
• You have very specific terminology (legal, medical, insurance, engineering sector) that the generic model doesn't handle with precision
• You want to classify according to your own taxonomy with dozens of nuanced categories
• You are extracting data from documents with proprietary formats (supplier invoices, internal delivery notes, contracts with custom templates)
• The cost of long prompts is skyrocketing due to high volume
• You need a small local model (that fits on your hardware) to perform like a large one, in a specific domain
• You want to preserve the tacit knowledge of experienced employees

Key sign: when prompts are already enormous, containing many examples of how you want it to behave, and even then the model deviates from the pattern in difficult cases, it is time to consider fine-tuning.

The Most Common Corporate Scenario: Combining All Three

In most real-world implementations, the three techniques coexist. Each one covers a different dimension of the problem:

• Fine-tuning defines how the model sounds and how it thinks (tone, criteria, classifications, format)
• RAG gives it access to updated and specific company information
• Prompting adjusts the behavior for each specific query

A typical example: a customer service chatbot for an insurance company. The model is fine-tuned with thousands of real responses so that it sounds like the company and handles sector terminology. In real-time, it consults with RAG the client's specific policy, claims history, and current coverages. And with every query, a structured prompt reminds it of the conversation context and the channel (web, mobile, email).

None of the three techniques alone would solve this case. All three together would.

Quick Decision Table

To decide which to apply to a specific problem:

Symptom	Appropriate Tool
The model doesn't respond in the format I want	Prompting
The model doesn't know my products, prices, or policies	RAG
The model knows outdated information	RAG
The model sounds generic, not like my company	Fine-tuning
The model doesn't handle my sector's terminology well	Fine-tuning
My prompts are enormous and I'm paying a lot in tokens	Fine-tuning
I need to classify tickets according to 20 specific categories	Fine-tuning
I want it to respond about this specific client case	RAG
I want to ensure always the same brand tone	Fine-tuning
I want it to follow a simple rule ("never talk about prices")	Prompting
I want to extract data from documents with proprietary formats	Fine-tuning

Real Costs: What Each Option Implies

Part of the decision is economic. These are the approximate orders of magnitude for an SME in 2026:

Prompting

• Development Cost: Hours, not days. Iterating on the prompt until the result is refined.
• Operation Cost: The token usage of each query. Long prompts multiply the cost per query.
• Maintenance: Low. If the prompt works, you rarely have to touch it.

RAG

• Development Cost: Days or a few weeks. Preparing documents, configuring the embedding and search system, integrating with the model.
• Operation Cost: Storage of embeddings (vectorial) plus queries to the model. Reasonable.
• Maintenance: Medium. You must keep the documents updated and reindex them when they change.

Fine-tuning

• Development Cost: Weeks. Preparing and anonymizing data, training, evaluating, iterating.
• Operation Cost: If you run the model on your infrastructure, only electricity. If you use a cloud service, it depends on the volume.
• Maintenance: Medium-High. When the domain changes significantly, you must retrain.

The common trap is thinking that fine-tuning will be cheaper in the long run because it reduces the cost per query. This is true at high volume, but only if the project reaches production. Many fine-tuning projects are abandoned earlier due to lack of quality data or changes in requirements. Doing prompting and RAG first also helps to validate whether the use case justifies the investment in fine-tuning.

Typical Mistakes When Deciding

Some patterns we see repeated that should be avoided:

"We are going to fine-tune directly"

Jumping to fine-tuning without having tried prompting or RAG because it sounds more powerful. Usual result: spending weeks of work, the model doesn't clearly improve compared to a well-written prompt, and the project is abandoned.

"RAG is the solution to everything"

RAG is very useful when the problem is lack of knowledge, but it doesn't fix the tone, it doesn't fix complex classifications, and it doesn't reduce long prompts. Some companies set up elaborate RAGs without realizing their problem was something else.

"A huge prompt solves it"

There are 3,000-token prompts trying to cover every possible case. They are expensive, slow, and difficult to maintain. When the prompt becomes a document, it is a sign that RAG or fine-tuning would do the job better.

"Fine-tuning on data that changes every month"

Training a model with information that will quickly become outdated. The correct approach is to fine-tune the behavior (which is stable) and use RAG for the information (which changes).

"We fine-tune without quality data"

Training with examples that are not representative, that have errors, or that are too few. Fine-tuning with bad data produces models worse than the original. The investment of time in preparing the data well is the largest part of the project, not the training itself.

A Practical Decision Process

If your company is evaluating customizing AI and doesn't know where to start, this is the sensible order:

1. Define the specific use case: a specific task, with a measurable result. Nothing like "improve the company's AI" in abstract terms.

2. First, test with prompting. Invest a few days in iterating on the prompt. Measure the results. In many cases, the story ends here.

3. If the problem is lack of knowledge, add RAG. If the model already knows how to behave but needs real data, this is the missing piece.

4. If consistency, tone, or precision is still lacking after prompting and RAG in specific tasks, consider fine-tuning. You already have a solid basis to decide if it is worth it.

5. Start fine-tuning with a small pilot: a limited use case, a few thousand examples, clear evaluation of whether it improves or not. If the pilot works, scale up.

This process avoids the most expensive mistake: spending weeks on the wrong technique because it sounded good on paper.

How We Can Help

At Navel Digital, we accompany companies in the process of deciding which technique applies in each case. Before writing code or training anything, we analyze the real problem and propose the approach that has the best relationship between cost, time, and result.

When prompting is enough, we help you design robust and maintainable prompts. When the problem is lack of real knowledge, we set up RAG systems connected to your documentation. And when the case justifies going further, we train specific models for your business, preserving the privacy of the training data and ensuring that the resulting model provides a measurable benefit.

If your company is starting to encounter the limits of generic models and want to know which technique truly applies to your case, contact us at no obligation.