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How To Nail Your Next Tech Interview

Leveraging AI Prompt Engineering to Transform Data Pipelines: A Guide for Data Engineers
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Leveraging AI Prompt Engineering to Transform Data Pipelines: A Guide for Data Engineers

According to the World Economic Forum, 97 million new job opportunities where machines and humans will collaborate to work together will surface by the year 2025. One of those jobs is being an AI prompt engineer, as generative AI models like ChatGPT are overtaking the world today.

Prompt engineering is becoming an aid for data engineers to leverage and strategize the data pipeline workflow. Clear prompts can ease the workload of any data scientists or engineers as prompts are input instructions that generate precise outputs according to your preference.

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

  • Prompt Engineering: Definition and Impact on Data Science
  • How Prompt Engineering Can Improve Data Pipeline for Data Engineers
  • Strategies to Format Effective Prompts for Data Engineers
  • Example of Prompt Writing in Data Engineering
  • Acing Data Engineering Interviews with Interview Kickstart
  • FAQs on AI Prompt Engineering

Prompt Engineering: Definition and Impact on Data Science

Prompt engineering is used to guide large language AI models to generate an accurate response with prompts and instructions. Prompt engineering plays a significant role in training data models with AI-driven data strategies. Prompt engineering is helpful for problem-solving methodologies that optimize AI models and provide solutions for the technical challenges of any business. 

In the AI world, prompt engineering refers to the conversational and effective approach of formulating instructions in the form of textual prompts to data models in return for the desired output. An AI prompt engineer can perform context integration, research, and evaluation of AI models to optimize and fine-tune them for specific purposes.

Prompt Engineering Impact on Data Science

As automation is the first thing we look for in software, prompt engineering in data science helps data engineers to upscale and automate their workflow. An AI prompt engineer can wear multiple hats, as they can use concise and output indicator prompts with relevant context. It is used for data engineering tasks such as data extraction, clustering prompts, tuning the hyperparameters, prompts to explore and pre-process data, writing tests, writing technical documentation, and much more.

It is a helpful tool for AI in data pipeline optimization. It uses effective prompts to generate required output from different language AI models like Transformer, Generative pretrained Transformer models, Large Language models, Encoder - Decoder-based models, memory optimization, and much more.

How Prompt Engineering Can Improve Data Pipeline for Data Engineers

AI prompt engineering with enhanced prompts
Forbes

Prompt engineering can improve multiple aspects of a data pipeline and deliver transformative optimization and performance improvements to make a system more robust and seamless. Some of the aspects of data engineering pipelines that can be improved by good prompt engineering are discussed below:

  1. Data collection

The very first step of a data pipeline is the collection and scraping of data. The data is not easily available, so with the help of systematic and foolproof pipelines, scripts are needed to fetch the required data from various sources. Defining the correct data sources, passing the correct rate limit values in the prompt so that the model’s generated code is accurate, and ensuring the prompt focuses on exception handling as multiple sources have different structures of handling data should be kept by AI prompt engineers. 

  1. During Data Ingestion and Storage

When the process of data collection is completed, and the required data is gathered, the storage system needs to be implemented. Writing prompts to help AI in data pipeline optimization at this stage should focus on efficient data storage, prompts to write logs with every operation, and prompts to create a record of data streaming sources. Using data storage like SQL can require writing complex queries. 

With the help of various prompting techniques and context learning, a model can be made to output multiple logical queries for joins, retrievals, etc., to ingest data in a database.

  1. Prompting during Data Cleaning

data collected from different sources can be unusable at first glance. A very crucial step in the data engineering pipeline is data cleaning. Scriptwriting to clean large volumes of data in a fast manner can come across as challenging to some data engineers. 

With proper prompt guidelines and AI-assisted coding, models can be made to output programs to replace missing variables in data, remove and handle outliers among all variables in data, and remove duplicates with opting. With correctly fed examples, example-based learning can help models output more refined data from raw sources.

  1. Prompting For Data Processing

In the ETL (Extract, Transform, and Load) Pipeline, the transformation part comes under the family of data processing steps. After the storage, for the data with a huge amount of rows and columns, filtering, aggregation, and transformation can be a heavy task for the data engineers. 

AI prompt Engineers can ease this workflow by creating prompts that take the input data and output the transformed data. Whether it be mathematical operations,  data type transformation, or creating new data records from old ones, correct prompting with the addition of edge cases can result in superior results.

  1. Data Modeling Process

Data models are the backbone of a data engineering pipeline project, and establishing relationships between data can often be daunting. Large models, often with carefully crafted prompts, can assist with choosing whether a relational or non-relational data model is required based on the characteristics of the data. 

With correct instructions by AI prompt engineers, schema design, data type selection, and the nature of data for the data analysis can be set. The model can also be instructed to write scripts to define relationships and also be fed the wrong ones to improve the output iteratively, and even code comments can be added.

  1. Analyzing the Data with Prompts

To get the hidden picture behind the data and to move further in the data engineering pipeline, data analysis should be done to understand the processed data from top to bottom fully. Data engineers can use large language models for code generation to write the program for analysis and visualization of stored data. Prompts can be written for statistical analysis, to calculate the relatability of data points, etc. 

Even dealing with time series data, prompt writing techniques like RAG or Tree of Thoughts, and using natural and clear language can result in programs dealing with them and even more complex data types.

  1. Data Validation and Security

Sometimes engineers can deal with sensitive data, like government records of citizens or pipelines for image data processing and storage for a big social media company. The sensitivity of data makes it more vulnerable to malicious attacks on pipelines. AI prompt engineers can generate complex encryption scripts with generative models by writing prompts with reasoning techniques. 

To check for data leaks, integrity, accuracy, and consistency of data, AI models can produce checker code blocks to keep an eye on data and maintain consistency and security throughout the automated data engineering pipeline.

  1. Scalability and Performance

With the ever-growing amount of data in 2023, larger models are being trained, and more data is getting accumulated every decade. Any pipeline that is to be implemented to extract, transform, and load this vast amount of data should always be robust, scalable, and top-notch in performance. 

Programs for frameworks like Apache Spark can be coded with AI models by feeding context-related information within prompts to process big data parallel. Caching and memoization can also be implemented, and faster experimentation can be done with the output by prompts and generative AI models. The same can be done for data compression to speed up the pipeline.

Strategies to Format Effective Prompts for Data Engineers

Data engineers work with extensive data pipelines all the time. Prompt engineering can be a game-changer as it can help them define the data objectively and write open-ended prompts alongside data evaluation and analysis.

  • Use-Case Understanding

When you are generating any prompt for a business strategy, make sure to be detailed in a conversational tone for generative AI to understand your input prompt. Instead of writing, “Sort [dataset] into a table,” you can write, “Analyze the [dataset] and sort the table into different columns according to the marketing growth of the product for every month of 2023.”

  • Avoid Vague Prompts

Although you, as an AI prompt engineer and data engineer, want your expected output to be optimized and strategic for technical requirements. Avoid using purposeless information and stuff the prompt with irrelevant data. You can provide a background context to make the AI model understand the scenario better, which can generate more optimized outputs for your prompt.

  • Avoid Complex Jargon Language

Large language models using generative AIs can easily get confused with domain-centric terms. The model can understand complex instructions if you craft them into simple prompts. If you want the AI model to process and streamline your instructions, you need to write to-the-point prompts, which can help you achieve the desired results for complicated tasks.

  • Readability and Conciseness of Code

The written code as a prompt for any task should not contain any filler parameters and should be easy to read and understand with a coherent structure. In the same manner, break down the prompt into smaller texts to make it easy for a generative AI model to understand your request.

  • Experiment with Prompts

Suppose you come across a detailed prompt to optimize the time complexity of the code. Still, as an AI prompt engineer or data engineer, everyone writes and tailors the code that suits their technical requirement. So, experiment with the different tones and ways to make data processing with AI smoother. Find the style that aligns perfectly with your business requirements as an engineer.

Example of Prompt Writing in Data Engineering

All the prompt writing techniques, when put to use in the various aspects of data engineering, yield very effective outputs. Let us look at the process:

  1. Regardless of which stage we are in our data engineering pipeline, the task should be understood, and prompt creation should be carried out.
  2. The greater the amount of detail and edge cases in the prompt, the more accurate the outputs will be 
  3. Let us write a prompt for data analysis of the CSV file:

Given the following CSV, Act like a Data engineer, and please provide the code to create a new variable apart from two currently present columns:

Index Height(Inches)" "Weight(Pounds)"

1 65.78 112.99

2 71.52 136.49

3 69.4 153.03

4 68.22 142.34

5 67.79 144.3

6 68.7 123.3

7 69.8 141.49

8 70.01 136.46

9 67.9 112.37

10 66.78 120.67”

  1. This prompt produces an accurate output:
 Prompt output with AI prompt engineering

  1. Furthermore, we can refine the prompt to ask for data visualization code with the prompt:

“Please provide the code for data visualization of height and width columns using seaborn.”

Refined output with AI prompt engineering

Acing Data Engineering Interviews with Interview Kickstart

Generative AI models and artificial intelligence, in general, have shocked the world with their technical capabilities. Unraveling job opportunities are surfacing in the tech industry, such as AI prompt Engineer, Data scientist, data engineer, and much more. That’s why many business entities are looking forward to hiring software engineers who are ready to upscale and learn prompt engineering to make the best use of AI in their organization.

Interview Kickstart is at your service to make you fully bullet-proof for your next tech interview. You can land your next high-paying job in data science and AI with our data science interview course. Accelerate your career at full speed and crush your next interview by joining a FREE webinar conducted by the industry expert today.

FAQs on AI Prompt Engineering

Q1. How do I improve my AI prompts?

As an AI prompt engineer, you can incorporate the best practices, like breaking down your complex prompt into small, simple texts and writing specific but relevant prompts. Learn about different writing and conversational styles according to your industry to communicate with the AI model.

Q2. What skills do you require for an AI prompt engineer?

You need to master programming and coding skills. Develop an understanding of AI, ML, LLMs, natural language processing, data analysis, feedback and testing, etc.

Q3. What are some types of prompting in AI?

There are different types of prompt engineering techniques, such as zero-shot prompting, iteration prompting, chain-of-thought prompting technique, etc.

Q4. How long can an AI prompt be in AI models?

An AI prompt can be as long as the model input context length (up to 4096 tokens in most of the cases).

Q5. What does an AI prompt engineer do?

An AI prompt engineer creates high-quality input queries, which results in maximum utilization of a large language model.

Last updated on: 
January 4, 2024
Author

Soham Mehta

Co-Founder - Interview Kickstart

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