RAG vs Fine-tuning vs Prompt Engineering: Choosing the Right Approach

Generative AI adoption and benefits are increasing every day. Organizations are also rapidly experimenting with Generative AI solutions to solve their business problems.

However, there are several ways to implement Generative AI development solutions. The three popular implementation approaches include prompt engineering, fine-tuning, and retrieval augmented generation.

Prompt engineering is the fastest way to leverage the benefits of AI. LLM fine-tuning is an ideal approach for specialized and more stable domains. And RAG is the most enterprise-friendly and flexible option to work with internal data sources.

But which approach could be the best for your business operations?

When it comes to incorporating AI solutions into businesses, a common question leaders often face is: How can we maximize the value of AI using our organization's own knowledge base and data? And which one could be the most suitable approach for that?

Well, if you are also looking for answers to these questions, this blog is for you. In this blog, we will compare the three approaches - RAG vs fine-tuning vs prompt engineering to provide you with clarity.

Retrieval Augmented Generation (RAG)

RAG is an advanced AI model architecture that combines two AI techniques- retrieval system and generation. It provides highly accurate and contextually relevant answers to users’ queries. RAG is a system that combines the strengths of language models with your enterprise's internal knowledge. Instead of retraining the model, RAG finds relevant documents or snippets in real-time and uses them in the prompt.

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The retrieval component first searches a large knowledge database for relevant information. This information is then passed on to a generative model like LLM to create a coherent and precise response.

The retrieval component first searches a large knowledge database for relevant information. This information is then passed on to a generative model like LLM to create a coherent and precise response.

The RAG architecture is particularly used in applications that require high accuracy and detailed information, such as customer support, research assistance, and technical documentation. RAG models are usually designed to be adaptable in order to fine-tune to specific knowledge bases or industries.

Did you know?💡

Gartner predicts Gen AI wil transform customer support and services by 2028. 80% of customer service and support organizations will be applying generative AI technology in some form to improve agent productivity and customer experience.

Pros of RAG

• Uses your internal knowledge securely

• Extracts real time data from external data sources

• Easy to update

• No retraining required

• Transparent & auditable

Cons of RAG

• Slightly more complex setup initially

Use Cases of RAG for Enterprises

RAG offers different use cases for enterprises. In Enterprise Knowledge Management, RAG can be used in internal chatbots that reference internal resources and the database. RAG in Customer Service support bots that fetch information from product manuals and ticket histories. It also supports voice assistants in answering billing, service, or warranty questions.

Also Read:  How RAG Improves Large Language Models to Deliver Real Business Value

Fine Tuning

Fine-tuning represents a method for adapting a foundation model or pre trained language model to a target task. In this process, a pre-trained model is further refined to excel in a particular domain, moving beyond the limitations of generic models.

40% of enterprises using generative AI rely on fine-tuned models for competitive advantage. Organizations often choose to fine tune large language models when they require distinct advantages such as improved model's ability, higher accuracy, and specialized performance for domain-specific applications.

Fine-tuning involves training the LLM on a dataset particularly tailored to a specific task, which includes input and output pairs that demonstrate the target task and desired outcomes. A 2024 study by Stanford's Center for Research on Foundation Models (CRFM) revealed that fine-tuned LLMs outperform their base models by an average of 63% on domain-specific benchmarks.

For example, fine-tuning a model on legal documents enhances the model's ability to understand legal language, reasoning, and citations. This makes it highly relevant for legal AI applications. It can provide accurate results in well-defined domains and help achieve desired outcomes.

Pros of Finetuning LLMs

• Highest control levels

• Versatile as finetuning can be applied to varied models and tasks.

• Tailored to specific domains

• High Accuracy

Cons of Finetuning LLMs

• Can be expensive

• Time consuming

• Less Flexible

• Technical Expertise Needed

• Data Dependency

How does Fine-Tuning Work?

Fine-tuning involves adjusting the internal parameters of large language models using custom datasets. It works in the following process steps:

Data collection involves gathering examples relevant to the tasks, like customer support chats. Dataset preparation consists in formatting the data into input and output pairs. Model training uses machine learning tools to update the LLM's parameters to make it more accurate for the specific tasks. The deployment phase uses the fine-tuned models in the product through an API or server.

Use Cases of Fine-Tuning for Enterprises

Fine-tuned LLM models can benefit businesses in many ways. Foe example, in the legal & compliance industry, fine-tuned LLM can be used for contract clause identification and risk scoring.

In the Healthcare industry, fine-tuned LLMs can be deployed as diagnostic assistants fine-tuned on clinical documentation. They can also perform patient data abstraction. In FinTech, LLM fine-tuning can be applied for fraud detection models trained on historical case data.

Prompt Engineering

Prompting is one of the easiest ways to interact with large language models. It is just similar to chatting with a friend. In prompt engineering, you provide LLMs with specific prompts about the type of information needed and its format. Prompt engineering is used to influence the model's behavior and guide the output toward specific goals.

By crafting optimal prompts, you can maximize the effectiveness of the model’s output and achieve the best results. This approach is valued for its cost efficiency compared to other methods.

Pros of Prompt Engineering

• Easy to deploy

• Works well with the general-purpose AI models like Gemini and Claude

• Good for low-risk use cases

Cons of Prompt Engineering 

• Not connected to your internal data

• Inconsistent as complexity grows

For example, whenever you use an LLM, say Gemini or GPT 4.0. You chat like- "Write a fitness retail product description." This is considered a basic prompt. A very generic one with no trained instances. However, when talking about prompt engineering, the LLM is well-trained to give an accurate answer. For instance,

Engineered prompt: Write a 100-word product description for the fitness audience, highlighting the smart watch's key traits. Highlight its waterproof design, heart rate monitor, and activity tracking in a persuasive and friendly tone.

This prompt will provide a specific outcome based on the trained instructions.

How Prompt Engineering Works?

AI Prompt engineering doesn’t change the large language models. In fact, it works with the model’s existing knowledge, trains it with different prompts until it achieves the best outcomes. The goal is to develop optimal prompts through iteration and testing, ensuring the most effective and efficient results from the model. The technique involves the following:

Use Cases of Prompt Engineering for Enterprises

Prompt engineering can be used in a number of ways in varied industries. In Marketing Automation, Prompt engineering can be beneficial in generating blog headlines, ad copy, or product descriptions. It can also help create email campaigns using tone-specific prompts or even draft social media calendars with custom brand guidelines.

In Customer Support, Prompt Engineering can be used in answering FAQs using model knowledge without internal data integration. Now that we already had an understanding of all the approaches, let us find out how these are different in our next section.

RAG vs Fine-tuning vs Prompt Engineering: Key Differences

Each approach differs in how it influences model outputs and helps achieve desired outcomes, depending on the specific project goals and requirements. Take a glance at quick differences between RAG vs Fine tuning vs Prompt engineering.

We will now explore the differences between these approaches in a detailed comparison.

Implementation Complexity

RAG has moderate implementation complexity. This model requires architectural and coding skills to implement the retrieval mechanisms with generative models. Fine-tuning has a higher complexity. This is because it involves retraining models on specialized datasets and adjusting model parameters.

Prompt engineering has low implementation complexity. This is because it just involves refining input prompts rather than altering the model itself.

Output Quality of the Large Language Models

RAG provides high-quality outcomes of the LLMs. It enhances the model's output with contextually relevant retrieved information and provides up-to-date data from external data sources, improving both relevance and accuracy.

Fine-tuning offers high-quality outcomes as these are tailored to specific datasets. This ensures the model's output is more relevant and accurate for the intended use case. The outcome of prompt engineering is highly variable. This is because it is dependent on the skill in creating prompts, which directly influences the model's output in terms of relevance and accuracy.

Skills Level Required to Use the LLM

RAG requires a moderate skill level, as it involves a basic understanding of both machine learning and information retrieval systems. Fine-tuning requires a high skill level due to its reliance on knowledge of machine learning principles and model architectures. Prompt engineering needs a low skill level as a basic understanding of how to construct prompts can do the needful.

Cost Analysis of RAG vs Finetuning vs Prompt Engineering

The choice between RAG, fine-tuning, and prompt engineering also depends on budgetary constraints.

RAG can be resource-intensive and involves setting up and maintaining an external database for retrieval. So, the costs involved are moderate to high. In terms of cost efficiency, RAG offers a balance between performance and resource expenditure, but may not be as cost-efficient as prompt engineering.

Fine-tuning needs additional data and significant computational resources for training. For example, fine-tuning a 7B parameter model can cost anywhere from $1,000 to $3,000. So, the costs involved in fine-tuning are higher. Fine-tuning is generally less cost-efficient compared to both RAG and prompt engineering due to its higher resource requirements.

Prompt engineering requires the least resources, as it uses the model as-is. So the costs involved in prompt engineering are less. Prompt engineering is the most cost-efficient approach among the three, achieving results with minimal resource expenditure.

Data Requirements

RAG has medium data requirements. This is because it needs access to relevant information sources or external databases to retrieve data. The data requirements in fine-tuning are high. This is because it requires a relevant and large training data for effective fine-tuning. Prompt engineering has no data requirements. It uses pre-trained language models without any additional data.

Also Read : Enhancing AI Interaction: A Guide to Prompt Engineering

RAG delivers better quality results by using relevant context directly from organized information sources. It significantly reduces mistakes, known as AI hallucinations.

Fine-tuning offers highly accurate results, often similar in quality to RAG. This method updates the model's settings based on specific data. This allows it to give more relevant responses.

In agricultural AI experiments, RAG boosted accuracy by 5 percentage points and combining RAG with fine-tuning increased accuracy cumulatively.

Prompt engineering focuses on providing ample context with a few examples to help the model understand your specific situation. While the results may look good on their own, this method usually produces the least accurate responses compared to other approaches.

Flexibility to Change

RAG offers great flexibility for making changes to the architecture. You can change the embedding model, vector database store, and LLM independently with minimal impact on the other components.
Fine-tuning a model is less flexible. If there are changes in data or inputs, the model needs another fine-tuning cycle, which can be complicated and time-consuming. Also, it takes considerable effort to adapt a fine-tuned model for a different use case. The same model's weights and parameter-efficient fine-tuning might not work well in areas other than the one for which it was originally tuned.

Prompt Engineering is very flexible. You only need to change the prompt templates based on the updates in the FM and the specific use case.

Maintenance

RAG requires low to medium maintenance efforts. It mainly involves updating the external knowledge base or indexing new documents. So, there is no need to retrain the model. Fine-tuning needs high maintenance efforts. This is because it requires retraining when data changes, monitoring for model drift, and regular performance evaluation. Prompt engineering requires low maintenance efforts. It requires minimal upkeep, as it can adjust prompts as requirements evolve.

RAG, Fine-Tuning or Prompt Engineering: What Should Your Enterprise Choose?

After analyzing it all the discussion narrows down to only one question. Which one should your enterprise choose?
Well, it truly depends on your unique business requirements and the desired outcomes you want to achieve. Some organizations choose to use both RAG and other techniques like fine-tuning or prompt engineering together, as this hybrid approach can deliver more accurate, current, and reliable results. The choice of approach should always align with the specific goals and desired outcomes of your project. To help you refine the choice, below are some scenarios where choosing one could be ideal.

Bottom Line

Choosing between prompt engineering, retrieval-augmented generation (RAG), and fine-tuning depends on your needs. Prompt engineering is a cost-effective and flexible way to improve how a model performs by refining prompts. RAG combines retrieval methods with generative models to provide rich and context-aware responses. Fine-tuning improves accuracy by retraining the model on specific datasets for particular tasks.

By understanding the strengths and weaknesses of each method, you can make better choices to use AI models effectively in your projects. If you are still unsure which approach is right for you, we are here to make things easier. Our  AI strategy and consulting experts can help you select one. Get in touch today.