RAG vs. fine-tuning vs. prompt engineering

Generative AI models are trained on massive pools of data, much of which is gleaned from the internet. Artificial intelligence developers do not typically have access to niche data, such as an enterprise’s proprietary and internal data. When organizations want to apply large language models (LLMs) to for specific needs, they need to tweak the way that the gen AI model works to produce the desired outputs and behavior.

Prompt engineering, RAG and fine-tuning all help optimize an LLM’s outputs for target use cases. With them, data scientists can get better downstream performance, greater domain-specific accuracy and output that meets relevant formatting, language or regulatory requirements.

The difference between prompt engineering, RAG and fine-tuning covers four primary areas of distinction:

• Approach

• Goals

• Resource requirements 

• Applications

Prompt engineering optimizes input prompts to steer a model toward better outputs. Fine-tuning LLMs trains them with domain-specific datasets to increase performance in downstream tasks. RAG connects an LLM to a database and automates information retrieval to augment prompts with relevant data for greater accuracy.

RAG, prompt engineering and fine-tuning have the same broad outcome: enhancing a model’s performance to maximize value for the enterprise that uses it. But more specifically, prompt engineering should lead a model to deliver the results the user wants. RAG aims to guide a model toward giving more relevant and accurate outputs. 

Meanwhile, a fine-tuned model is retrained on a focused set of external data to improve performance in specific use cases. The three methods are not mutually exclusive and are often combined for optimal outcomes. 

Prompt engineering is the least time-consuming and resource-intensive of the three optimization techniques. Basic prompt engineering can be done manually without any investment into extra compute.

RAG requires data science expertise to organize enterprise datasets and construct the data pipelines that connect LLMs to those data sources. Fine-tuning is arguably the most demanding because the data preparation and training processes are so compute-intensive and time-consuming.

Prompt engineering is the most flexible and shines in open-ended situations with a potentially diverse array of outputs, such as when asking an LLM to generate content from scratch. Image, video and text generation success thrive on strong prompts.

Fine-tuning hones a model for highly focused work—when data scientists need a model to do one thing very well. RAG is an ideal solution where accurate, relevant, current information is paramount, such as with customer service chatbots.

Prompt engineering offers a range of methods for giving models explicit instructions on how to behave. With clear directives, model behavior can be more precisely sculpted without having to invest in resource-intensive retrieval systems or training.

RAG plugs an LLM into proprietary real-time data that would otherwise be inaccessible to it. RAG models can return more accurate answers with the added context of internal data than they otherwise would be able to without it.

A fine-tuned model typically outperforms its corresponding base model, such as those in the GPT family, when applying its training with domain-specific data. With greater access to external knowledge, a fine-tuned LLM has a better understanding of the specific domain and its terminology.

Prompt engineering is the process of creating effective prompts that guide a model toward desired outputs without expanding its knowledge base. The prompt engineering process does not significantly alter a pre-trained model’s parameters.

The goal of prompt engineering is to craft prompts that cause a model’s outputs to meet the specific requirements of the intended use case. Further training and greater data access cannot compensate for poor prompting.

Prompt engineering works by tweaking the structure and content of input prompts based on previous model outputs. With each iteration, the prompt engineer learns how the model responds to prior inputs, then uses those results to inform the next prompt. The goal is to modify model behavior through clear instructions.

Good prompt engineering is based on prompts that tell a natural language processing (NLP) model exactly what to do. The prompt engineering process involves experimenting with the prompt’s content, structure and language to discover the optimal format that leads to the needed output from the model.

Compare a machine learning model to an aspiring home cook who wants to make a great dinner. Prompt engineering would be analogous to a more knowledgeable friend or relative who helps them plan their approach to the meal. With solid advice on what to make and how, the eager home cook is more likely to produce something delicious.

RAG is a data architecture framework that connects an LLM to other data, such as an organization’s proprietary data, often stored in data lakehouses. RAG systems add relevant data to LLM prompts so the LLM can generate more accurate answers.

Retrieval augmented generation works by locating data that is relevant to the user’s query, then using that data to create more informative prompts. An information retrieval mechanism is added to augment the prompts for the LLM and help it generate more relevant responses.

RAG models generate answers through a four-stage process:

1. Query: a user submits a query, which initializes the RAG system.

2. Information retrieval: complex algorithms or APIs comb internal and external knowledge bases in search of relevant information. 

3. Integration: the retrieved data is combined with the user’s query and given to the RAG model to answer. Up to this point, the LLM has not processed the query.

4. Response: blending the retrieved data with its own training and stored knowledge, the LLM generates a contextually rich and accurate response.

When searching through documents, RAG systems use semantic search. Vector databases organize data by similarity, thus enabling searches by meaning, rather than by keyword. Semantic search techniques enable RAG algorithms to reach past keywords to the intent of a query and return the most relevant data.

RAG systems require extensive data architecture construction and maintenance. Data engineers must build the data pipelines needed to connect their organization’s data lakehouses with the LLM and use RAG. RAG systems also need precise prompt engineering to locate the right data and make sure the LLM knows what to do with it.

Again, imagine a gen AI model as an amateur home cook. They know the basics of cooking but lack the latest information and expert knowledge of a chef trained in a particular cuisine. RAG is like giving the home cook a cookbook for that cuisine. By combining their general knowledge of cooking with the recipes in the cookbook, the home cook can create their favorite cuisine-specific dishes with ease.

Fine-tuning is the process of retraining a pretrained model on a smaller, more focused set of training data to give it domain-specific knowledge. The model then adjusts its parameters—the guidelines governing its behavior—and its embeddings to better fit the specific data set.

Fine-tuning works by exposing a model to a data set of labeled examples. The model improves on its initial training as it updates its model weights based on the new data. Fine-tuning is a supervised learning method, which means the data used in training is organized and labeled. By contrast, most base models undergo unsupervised learning in which the data is unsorted—the model must categorize it on its own.

Once more imagining a gen AI model as a home cook, fine-tuning would be a cooking course in a specific cuisine. Before taking the course, the home cook would have a general understanding of cooking basics. But after undergoing culinary training and acquiring domain-specific knowledge, they’d be much more proficient in cooking that type of food.

Models can be either fully fine-tuned, which updates all their parameters or fine-tuned in a way that updates only the most relevant parameters. This latter process is known as parameter-efficient fine-tuning (PEFT) and is a cost-effective way to make models more effective in a certain domain.

Fine-tuning a model is compute-intensive and requires multiple powerful GPUs running in tandem—let alone the memory to store the LLM itself. PEFT enables LLM users to retrain their models on simpler hardware setups while returning comparable performance upgrades in the model’s intended use case, such as customer support or sentiment analysis. Fine-tuning especially excels at helping models overcome bias, which is a gap between the model’s predictions and actual real-world outcomes. 

Pretraining occurs at the very start of the training process. The model weights or parameters are randomly initialized and the model commences training on its initial data set. Continuous pretraining introduces a trained model to a new unlabeled data set in a practice known as transfer learning. The pretrained model "transfers" what it has learned so far to new external information.

By contrast, fine-tuning uses labeled data to hone a model’s performance in a selected use case. Fine-tuning excels at honing a model’s expertise at specific tasks, while continuous pretraining can deepen a model’s domain expertise.