Enhancing Code with AI: A Software Engineer’s Guide to Prompt Engineering

Is generative AI and prompt engineering the next hot trend in the AI realm? Prompt engineering enhances the performance of generative AI by altering the questions (prompts) to user-specific needs. In this prompt engineering guide, we’ll see what prompt engineering is, how to write an effective prompt, etc.

In a recent McKinsey Global Survey, 40% of business organizations are expected to invest in AI due to the evolution of generative AI. As the demand for AI in business integrations is expanding, software engineers are already mastering prompt engineering, and you can do it, too!

Here’s what we’ll cover in this article:

• What is Prompt Engineering in AI?
• Technical Points to Remember in Prompt Engineering
• Types of Prompt Engineering
• Significant Prompt Engineering Techniques
• Key Components to Format a Prompt
• Real-World Examples of Code Generation in Prompt Engineering
• Factors to Check the Correctness of a Prompt
• Role of a Prompt Engineer with Required Skill Set
• Give Wings to Your Career with Interview Kickstart
• FAQs on Prompt Engineering Guide

What is Prompt Engineering in AI?

Command writing in the form of prompts is an effective way to communicate with AI models. Among the AI-powered development tools, prompt engineering is setting its benchmark by optimizing the LLM model capacity to perform complex tasks of natural language processing in software engineering.

Prompt engineering can be defined as formatting accurate input prompts, which can be an image, code, or text that is helpful in determining the desired outputs as expected by generative AI models. The better the input prompt, the more high-quality the generated output response.

Prompt engineering is a process of curating, refining, and designing input prompts and specific instructions to trigger certain output responses from an AI model.

Technical Points to Remember in Prompt Engineering

There are some technical terminologies to remember before prompting, which are as follows:

• Token Count

There are thousands of models available for developers to ease their work, but all of them have different input token lengths. Make sure your prompt and data sources that are passed along don’t exceed the supported input context length and that the number of tokens is inside the allowed window. It can range from 2032 to 16000 on some newer models.

• Right Temperature

While calling an LLM to provide a prompt for AI code generation through the API for different coding methods, the flexibility increases as there are tuning parameters like Temperature, Top-p, Top-k, Stopping Symbol, Repetitive Penalty, etc., that are there to lead us to the desired output.

An increase in temperature can lead to more creativity but also make the model prone to hallucination. The repetition penalty punishes the model for generating similar words. These parameters need to be tuned to the task accordingly.

• The architecture of the Model

All the available models on the internet are based on various deep learning architectures, like transformers and encoder-decoder-based models, and use different forms of training data and training techniques, like reinforcement learning with human feedback.

Models like Falcon require a question-and-answer format to send the prompt through the API for code generation. In contrast, Open AI models have function calls to send the prompt directly without any stopping parameters.

Types of Prompt Engineering

Let’s see the types of prompt engineering below:

• Text completion prompts
• Instruction based prompts
• Multiple choice prompts
• Contextual prompts
• Bias mitigation prompts
• Fine-tuning and interactive prompts

Significant Prompt Engineering Techniques

Writing an efficient prompt that uses fewer tokens and gets the job done can be a tricky task, but with the right prompting techniques and workarounds, the path to getting the desired code becomes straightforward.

1. Zero-Shot Prompting Technique

As large language models are trained on a lot of data, smaller tasks that require simpler prompts can be performed using the zero-shot prompting technique. It takes one instruction to generate output and does analysis using the data on which the model is trained.

2. Prompting using the Few Shots Technique

To get the model to generate the continuation of a code or repeat the flow of a code, the few-shot prompting technique can be used. It involves sending a few demonstrations of tasks that need to be done to make the model learn from the context given and generate the proper output.

3. CoT Prompting

Chain of Thought Prompting is advanced few-shot prompting. When the asked programming output gets too complex for the model to respond to, a few similar examples paired with the reasoning and logic behind the output of those prompts are passed for the model to get an idea of how to answer the final prompt. Zero-shot CoT and AutoCoT are two further implementations of the Chain of Thought technique.

4. Self-Consistency Prompting Technique

Other techniques are relevant, but Self-consistency improves the ability of models to perform program generation, reasoning, arithmetic programming, and logical coding by a significant margin. It uses sampling techniques to choose the most consistent output from a diverse range of reasoning paths.

5. Prompting with Tree of Thought

For very intricate program generation that requires exploration of edge cases or planning, Tree of Thoughts works like wonder as in the step of solving a problem. The ToT technique allows the model to store thoughts, mixing the Large Language model’s ability to generate vivid output with the ability to search through that knowledge (which is stored as thoughts) with tree algorithms like Breadth of Depth First Search allows ToT to generate solutions to tough reasoning-based queries.

6. RAG Technique

If the prompt while doing AI-assisted coding needs external knowledge to create a response and to avoid hallucinating while generation, retrieval-augmented generation allows the addition of external knowledge sources like documents to share the data with LLMs, which then results in better in-context learning and a much more precise response to our prompt.

7. Active Prompting

Introduced in 2023, active prompting builds upon the Chain of Thoughts technique. Firstly, the prompt for the programming task is passed, and a few answers are generated. A parameter is set to select the most uncertain responses among the output, and they are then sent to humans to provide better information for improvement in understanding of the model.

Key Components to Format a Prompt

If you want to create a prompt that is effective and generates outputs according to your preference, then some key components act as a building block of a good prompt, which is listed below in this prompt engineering guide:

• • Clear Instructions: It lays the foundation of an effective prompt and gives clear instructions to the AI model about your output expectations. For instance, instead of writing “Suggest ten best places to dine…,” you can write, “Suggest some best places to dine in XYZ city.”
  • Informational Input Data: It is a niche or domain-centric specific data that you want to give as input to the AI model. The input could be text, paragraphs, an image, code, statistics, etc.
  • Relevant Context: Providing a relevant example or context is helpful for the model to understand the background foundation of any prompt. For example, “As a social media manager, providing the strategy for an ABC campaign” can be helpful for the model to generate the detailed response.”
  • Indication of Output: The output indicator is crucial in the role-playing technique, where the “role-playing” element plays a pivotal role in obtaining the desired outcome. It can look something like “Rewrite these sentences in the tone of Oscar Wilde.”

Real-World Examples of Code Generation in Prompt Engineering

After reading a lot about how to write prompts, it is time to put that into practice.

1. The prompt for the task that needs to be carried out should be defined in the very first step. Let’s assume that the code generation needs to be done for Python to sort an array.
2. A basic prompt that could be:

“ Please provide a program to sort an array in Python. “

3. After entertaining the prompt in the model, we will get a response something like this:

4. In the above response, the models take the inbuilt modules in Python and solve our problem by generating output around that.
5. For better and more refined output, the prompt can be provided with extra knowledge for more efficient coding with AI. Let’s say for the improved prompt.

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6. The resulting output is much improved and ready to use in code.

Factors to Check the Correctness of a Prompt

Prompt writing should always be accompanied by correcting the prompt when needed and tweaking as per the requirement, whether it be code generation or code completion. To check the correctness of the prompt, the following factors should be kept in mind:

1. Correctness of Output

One of the questions that should be asked while writing a prompt by a software developer is whether the prompt will fully answer the question and consider all the factors and edge cases; if not, they should be added to the prompt for finer-generated code.

2. Compilation of Code

The prompt should always be changed depending on whether the output code compiles or not, whether the program is syntactically correct or not, or whether it follows the programming rules it’s supposed to follow. If not, the information should be fed before reiterating with the prompt.

3. Performance of the Program

Sometimes, the desired program needs to be efficient to be usable for software engineers, but due to a lack of prompt, the resulting code is inefficient and complex. In these cases, the conditions should be set in the prompt to make sure that the program meme achieves what’s asked in the prompt efficiently.

4. Completeness of the Output

One final check that also needs to be done while prompt creation for AI-assisted coding is that if the prompt fulfills all the conditions that will result in an acceptable output, vagueness should be avoided at all costs to get optimized code output from LLMs.

Role of a Prompt Engineer with Required Skill Set

Now, let’s see what a prompt engineer does in this prompt engineering guide. A prompt engineer curates, designs, and creates prompts, commonly known as questions, scenarios, and statements, with the help of text-written data, code, etc, to obtain the desired result with generative AI models like ChatGPT. The output optimization is dependent on the precise input.

Prompt engineers are slightly different from traditional engineers, as they have to be a creative thinker, a coder, and a writer. Any skilled prompt engineer understands the pain points of business and creates a strategic solution by prompting effective input that can provide a solution with the help of an AI model.

Required Technical Skills for a Prompt Engineer

There is a certain skill set that a prompt engineer must have according to the industry standards and business requirements. Let’s see some required technical skills in this prompt engineering guide.

• Knowledge of NLP: Concepts of natural language processing, its algorithms, and working must be clear to a prompt engineer.
• Understanding of LLMs: Transformer architecture knowledge and some experience with large language models like ChatGPT and BARD are a must.
• Knowledge of API: In order to integrate the generative AI models for better response, a prompt engineer must be familiar with API and how to use it.
• Data Analyzation: It is a keen skill that should be mastered to analyze the output responses and interact with the output generation pattern of the model.
• Testing iterations and experiments: Prompt engineers should be able to conduct tests like A/B tests and optimize the given input prompts on the basis of feedback to improve the model outputs.

Give Wings to Your Career with Interview Kickstart

We have unlocked the wide spectrum of prompt engineering in this prompt engineering guide for software engineers. The qualities of a good prompt engineer include writing precise texts, giving relevant examples, using a certain tone of voice, knowing the target audience, and the ability to boost a generative AI model that generates accurate outcomes.

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FAQs on Prompt Engineering Guide

Q.1. What do you mean by chunking in prompt engineering?

Ans. Chunking stands for dividing the prompt or text into smaller chunks to fit the input token length of the model.

Q,2. How long does it take to master and learn prompt engineering?

Ans. Firstly, you need to master the basics of LLMs and study the architecture of models, then learn writing prompts, which will take approx. 3-4 days to learn and two weeks to master, but it varies according to an individual’s pace.

Q.3. What is an example of one-shot prompt engineering?

Ans. As one-shot techniques can be used for object detection and classification of images, face recognition can be an example of it.

Q.4. What is the main purpose of using prompt engineering in Gen AI models?

Ans. Prompt engineering optimizes the creativity of a person to brainstorm a wide spectrum of ideas and possibilities using AI.

Q.5. What are the key fundamentals of prompt engineering?

Ans. Natural language processing (NLP) algorithms, frameworks, and libraries for vast data, Python programming language knowledge, and LLM understanding are some fundamentals for prompt engineering.