RAG or fine-tuning? Large model floor selection guide

More and more software testers are beginning to pay attention to how to integrate LLM (Large Language Model) into the testing workflow. But when we really want to "get our hands dirty", we often stand at a fork in the road: should we choose the RAG (Retrieval Augmented Generation) framework or do fine-tuning directly?

It's like there are two ways to modify an old car: one is to install a super-intelligent navigation system (RAG) that relies on external maps to quickly respond to various route changes; the other is to replace the entire engine (fine-tuning) to make it more adaptive from the bottom up.

So, how should testers make a choice? This article will take you to compare from four perspectives: learning cost, development efficiency, usage cost, and controllability .

In simple terms:

• RAG framework : It retrieves external knowledge bases (such as product documents and test specifications) in real time and feeds them to the big model for processing together with user input, thereby improving the relevance of answers.

• Fine-tuning : Based on the existing large model, use your own data to "retrain" the model so that it has the knowledge or style you want it to know.

in other words:

• RAG is more like a "real-time answer database" next to the model.

• Fine-tuning is to write this information directly into the model's "brain".

1. Learning curve: Which one has a lower entry difficulty?

For most testers, learning RAG will be more "friendly" than fine-tuning.

RAG is essentially a combination of advanced prompt engineering + vector retrieval database. There are already very mature open source tools on the market (such as LangChain and LlamaIndex). Just like prefabricated home furnishings, it is not too complicated to build.

In contrast, fine-tuning is much more troublesome. Not only do you have to prepare clean and structured data, but you also have to choose the right model architecture, configure training parameters and hardware resources, and finally verify the training results. The whole process is more like "customizing an intelligent robot."

✅ Recommendation: RAG is preferred in the initial trial or prototype stage; fine-tuning can be considered when there is a mature dataset and stable scenario.

2. Cost: Is RAG really cheaper?

• Tool cost : The initial cost of setting up RAG is low, but the long-term cost may soar. Especially when you call a third-party LLM API (such as OpenAI), the more tokens you use, the more money you spend.

• Talent cost : RAG does not have high talent requirements. Familiarity with API and basic data processing is sufficient. Fine-tuning requires more professional AI engineering knowledge, and the recruitment or training costs are higher.

• Hardware cost : RAG can be hosted and run in a lightweight environment, but fine-tuning requires high-computing equipment (such as a cloud server with a GPU).

So overall, RAG is more suitable for small and medium-sized teams to quickly trial and error, while fine-tuning is more suitable for teams with sufficient budgets and long-term investment plans .

3. Getting started: RAG is more like "unscrew and use"

The core work of the RAG framework is two things:

• Prepare external knowledge base (such as interface documentation, test cases).

• Design prompts and let the model generate answers based on these contents.

Therefore, its debugging rhythm is very fast, and different combinations can be tried quickly like seasoning. However, fine-tuning is equivalent to "cooking from scratch", which is a slower process and takes longer time for preliminary preparation and verification.

Metaphorically speaking, RAG is ready-made instant noodles, which is fast but may not be deep enough; fine-tuning is ramen noodles in soup, which is slow but may have a stronger flavor.

4. Controllability: To what extent do you want the model to "listen to you"?

One of the limitations of RAG is that it is not very controllable . For example, if you use a RAG tool on a certain platform, what is its vector search algorithm? How is the data stored? Does the API support calling the model you want to use? These may not be transparent.

Fine-tuning is different. You have full control over the entire process, including:

• what data is used;

• Which model to use;

• Deployed locally or in a private cloud to meet compliance requirements;

• Define the answer style and avoid model deviation.

In enterprise-level testing platforms, when models need to "remember" fixed specifications for a long time, fine-tuning control capabilities are preferred.

Whether you use RAG or fine-tuning, you cannot avoid a key concept: context window .

What is this? Simply put, it is the length of text that the model can "remember" in a conversation, usually measured in tokens.

• The context window of GPT-4 can reach 128k tokens;

• One A4 document page is approximately equal to 1000 tokens;

• The model will "forget" anything outside this range.

This means:

• When using RAG, the external data you attach must be kept within the context window , otherwise some of the information will be ignored;

• Although the fine-tuned model has its own knowledge, it may still give irrelevant answers if the scenario exceeds the context.

✅ Testing suggestions: Try to place key information close to the prompt, and pay attention to how many context windows your model supports to avoid redundant interference or missing key information.

In the future, the integration of big models and software testing will only deepen. RAG and fine-tuning are not two opposing choices, but different weapons in the toolbox. The key lies in the scenario you are facing and how much time and resources you are willing to invest in building your smart test assistant.