2024/08/08 | Written By: Suwan Kim

In the previous post, we defined Prompt Engineering, its components, and how to design prompts to generate effective outputs. To understand what Prompt Engineering is and how to improve efficiency through simple design, please refer back to our prior content!

In this blog, we will delve into the significance of Prompt Engineering and explore both fundamental and advanced Prompt Engineering techniques. Let's quickly learn the methodologies of Prompt Engineering with Solar!

The Importance of Prompt Engineering

Let us revisit what Prompt Engineering is. A prompt is an input or instruction given to the AI model to derive the desired output from an LLM. Prompt Engineering involves finding the optimal combination of these prompt inputs.

So, why is Prompt Engineering crucial? It directs LLMs to generate effective responses, much like how humans perform better with multiple examples and prior preparation. By giving LLMs examples or templates, you can significantly enhance the quality of their responses.

Techniques of Prompt Engineering

1. N-Shot Prompting

Have you ever heard of zero-shot, one-shot, and few-shot prompting? Let’s understand the differences now.

Zero-Shot Prompting
- Generate answers without any given examples, useful for assessing the inherent performance of LLMs.

One-Shot Prompting
- Provide a single example before obtaining a response.

Few-Shot Prompting

Entails multiple examples for more accurate answers.

Let's examine these techniques using Solar through sentiment analysis.

2. Chain-Of-Thought (CoT) Prompting

Chain-Of-Thought Prompting enhances LLMs' reasoning abilities through multi-step inference processes. Instead of structuring prompts as 'problem-answer,' they follow 'problem-step -answer.’ This is suitable for various tasks, including math, general knowledge, and symbol reasoning.

Consider the following example involving a mathematical problem.

The first example illustrates zero-shot prompting, where the model is asked to answer a given question without any prior examples. In contrast, the second example demonstrates one-shot prompting with a Chain-of-Thought (CoT) prompt, wherein a single CoT prompt is provided before the next question. The more examples you provide, the more accurate the model's responses will become.

This method can be applied to various tasks, such as solving math problems, understanding commonsense, interpreting dates, and reasoning with symbols. The following examples are responses generated using a few-shot Chain-of-Thought (CoT) prompt for commonsense questions.

commonsense example — Commonsense Example

3. Least-To-Most Prompting

This technique solves complex problems by breaking them into simpler sub-problems tackled sequentially.

Process:

Decomposing Prompt: Break down the complex problem into simpler sub-problems using an example.
Solving Prompt: Solve each sub-problem step-by-step using an example prompt to generate the final answer.

This approach maximizes the LLM's performance, especially for complex or creative problem-solving.

Let's apply the Least-to-Most Prompting methodology to solve some problems.

Initially, as illustrated in the image on the left, we employed a Decomposing Prompt to disassemble a complex problem into discrete sub-problems. Consequently, the LLM adeptly partitioned the problem into two steps.

Subsequently, the right image elucidates the application of a Solving Prompt to systematically tackle each sub-problem and arrive at the final solution. The LLM proficiently generated responses for each sub-problem, culminating in a comprehensive final answer.

By leveraging Least-To-Most Prompting, the LLM can further dissect the reasoning process, thereby delivering more precise solutions to intricate problems.

4. Self Consistency Prompting

An advanced iteration of Chain-Of-Thought (CoT) Prompting, Self Consistency Prompting mitigates errors that may arise from single-step reasoning by sampling multiple reasoning pathways and selecting the most consistent output.

Process:

Generate a Chain Of Thought (CoT) prompt.
Create multiple reasoning pathways.
Select the output that is most accurate and consistent.

This methodology significantly enhances the accuracy of complex problem-solving, albeit it may necessitate additional computation time and resources.

The self-consistency method contains three steps: (1) prompt a language model using chain-of-thought (CoT) prompting; (2) replace the “greedy decode” in CoT prompting by sampling from the language model’s decoder to generate a diverse set of reasoning paths; and (3) marginalize out the reasoning paths and aggregate by choosing the most consistent answer in the final answer set.
(Source: SELF-CONSISTENCY IMPROVES CHAIN OF THOUGHT REASONING IN LANGUAGE MODELS)

5. Generated Knowledge Prompting

Generated Knowledge Prompting allows the LLM to generate additional information or knowledge before responding to a user query. By tapping into the model’s internal knowledge base, this approach enhances the depth and accuracy of the responses.

Initial Response Without Generated Knowledge:

Generating Knowledge:
Let’s first generate some additional knowledge regarding the example statements. Here are some examples of prompt-generated knowledge:

Now, let’s use this generated knowledge in conjunction with the example statement.

Applying Generated Knowledge to the Example Statement:

It is evident that using generated knowledge allows for more structured and systematic responses. Generated Knowledge Prompting creates information internally from the model itself, thereby avoiding issues related to the quality and reliability of external knowledge sources. Furthermore, this method enables the LLM to provide more precise answers tailored to each specific context.

Summary

We have now examined various key Prompt Engineering techniques. Let us succinctly summarize each method in a single line:

N-Shot Prompting: Providing multiple examples to improve the accuracy of LLM responses.
Chain-Of-Thought Prompting: Enhancing problem-solving abilities through step-by-step reasoning processes.
Least-To-Most Prompting: Simplifying complex problems into manageable sub-problems for precise answers.
Self Consistency Prompting: Generating multiple reasoning pathways to ensure accurate solutions by selecting the most consistent output.
Generated Knowledge Prompting: Enabling LLMs to generate and utilize background knowledge for deeper, more precise responses.

In conclusion, Prompt Engineering is vital for maximizing the potential of Large Language Models. By understanding and applying these techniques, you can unlock the full capabilities of LLMs like Solar, driving more accurate and efficient outcomes. Your journey in mastering Prompt Engineering starts now—explore, experiment, and innovate!

Explore the Solar Playground

Head over to Solar Playground to put these techniques into practice. Engage in real-world experiments, create demonstrations, and see how different Prompt Engineering methods can enhance your work with LLM. Happy Prompting!

👉 Try our prompt engineering tips with Solar LLM

[Prompt Engineering - Part 2] The Essence of Prompt Engineering: A Comprehensive Guide to Maximizing LLM Usage