Surviving the LLM Jungle: When to use Prompt Engineering, Retrieval Augmented Generation or Fine Tuning?
Tags: llm, promptengineering, chatgpt, beginners
Published: 2023-09-25 18:28:24

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/jvb8291nqf4jko9v3eqr.jpg)

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# Introduction

Navigating the complex world of **Large Language Models (LLMs)** utilization can sometimes feel like wandering through an uncharted jungle. With a myriad of techniques at your disposal, choosing the right path can be daunting. In this blog, we explore three key strategies for harnessing the power of LLMs: **Prompt Engineering, Retrieval Augmented Generation, and Fine Tuning**. By the end of this article, you'll have a clearer understanding of when and how to employ these techniques to achieve your **Generative AI** goals.

## Prompt Engineering: Crafting the Right Query

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/k89rwkqunopr6o4ti3te.jpeg)

**Prompt engineering** is a technique used in the context of **Large Language Models (LLMs)** to **design** and **craft** effective prompts or input queries. The goal of prompt engineering is to **optimize** the input provided to the model to achieve desired outcomes, **improve** model performance, and guide the model to produce **more accurate** or **contextually relevant** responses.

Imagine the process of interacting with an **LLM** as a conversation between you and a highly knowledgeable but somewhat **literal-minded expert**. In this scenario, **prompt engineering** is akin to **formulating the right question**. This technique involves designing precise and effective prompts to elicit the desired responses from the model.

For example, if you want to generate a creative piece of writing, your prompt should be **open-ended** and **encourage creativity**. Conversely, if you seek specific factual information, your prompt should be **clear** and **structured**. Effective prompt engineering not only requires an understanding of your task but also a grasp of how language models interpret and respond to prompts.

## When to Use Prompt Engineering

* When you need **fine-grained control over the output**.
* When generating **specific**, **structured content**.
* When exploring **creative possibilities** by carefully designing prompts.

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## Prompt Engineering: Key Aspects and Considerations

* **Task Definition**: Here, you define a specific task or question you want the LLM to perform. This task could be anything from **language translation** and **text summarization** to **question answering** or even more specialized tasks like **image captioning** (although LLMs are primarily text-based).

* **Prompt or Query**: You then formulate a prompt or query for the model that specifies the task. The prompt serves as the input to the model.

* **Inference**: The LLM processes the prompt and generates an output based on the provided examples and task description. It leverages its pre-trained language understanding capabilities and generalizes from the limited examples to produce a response.

* **Evaluation**: You evaluate the model's output to determine if it successfully performed the task according to your requirements.

### Example

#### Text Generation Without Prompt Engineering

```
Prompt: "Write a product description for the new smartphone."
```

In this case, the prompt is relatively **vague**, and the LLM might generate a **generic** or **less informative** response because it lacks specific details about the smartphone.

#### Text Generation With Prompt Engineering

```
Prompt: "Write a compelling product description for the new XYZ Phone, highlighting its key features such as the 6.5-inch AMOLED display, Snapdragon 855 processor, dual-camera setup for stunning photography, and long-lasting battery life of up to 2 days."
```

In this engineered prompt:

* The model is **explicitly instructed** to write a *compelling product description* with a clear task.
* **Specific details** about the smartphone are provided, such as the **display size, processor, camera features, and battery life**.
* By mentioning *compelling*, you convey the expectation of **persuasive** and **engaging** language.

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## Retrieval Augmented Generation: Expanding the Horizon


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/nzzxwnoc1erotyl57qym.jpg)

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**Retrieval augmented generation (RAG)** is a technique that combines the strengths of **large language models** with **external knowledge sources**. It involves retrieving relevant information from a vast corpus of data and then using it to enhance the generation capabilities of the LLM. This approach can lead to **more accurate and contextually rich responses**.

For instance, when generating medical advice, you can retrieve the latest research papers and clinical guidelines to ensure that the information provided is up-to-date and evidence-based. This strategy allows LLMs to function as **dynamic encyclopedias**, offering **insights** and **recommendations** grounded in **real-world data**.

### When to Use Retrieval Augmented Generation

* When your task requires access to **external knowledge**.
* When you need to provide **accurate** and **current information**.
* When you want to enhance the **contextuality** of generated content.

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## Fine Tuning: Tailoring the Model to Your Needs


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/dzqi7o3msrp2zyqjqef9.jpg)
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**Fine tuning** involves training a pre-trained LLM on a specific dataset or task to adapt it to your unique requirements. This technique allows you to specialize a **general-purpose large language model** for a particular domain, making it more efficient and proficient in a specific area.

For example, if you are building a **chatbot for customer support** in the **fashion industry**, **fine tuning** can help the model understand and respond to **fashion-related** queries with greater accuracy. It **refines** the model's knowledge and behavior to align with the nuances of the domain in question.

### When to Use Fine Tuning

* When you have access to **domain-specific data**.
* When you want the model to **excel in a particular field**.
* When you need to **optimize** the model's performance for a **specific task**.

### Conclusion

In the vast LLM jungle, understanding when to use **prompt engineering, retrieval augmented generation, or fine tuning** is crucial for achieving your goals. These techniques offer versatile tools for tailoring **large language models** to your specific needs, whether you require **precise responses, access to external knowledge, or domain expertise**.

Remember: the choice between these techniques often depends on the unique demands of your project. Each of these approaches has distinct requirements - in terms of volume and quality of data, as well as costs - and also particular advantages and caveats. But that is the topic for another post.