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The Role of AI Prompt Engineers in Data Engineering: Enhancing Data Quality and Processing

Last updated on: 
January 5, 2024
Abhishek Som
The fast well prepared banner
About The Author!
Abhishek Som
Abhishek Som

Did you know that more than 60% of companies and organizations make use of generative AI models in their workspace? Consequently, the demand for models like ChatGPT, MidJourney, etc., is also increasing the demand for an AI prompt engineer. Prompt engineering is proving to be a groundbreaking innovation in the technology realm.

AI prompt engineering simplifies the data processing flow and enhances the data quality for data engineering processes with the set of instructions known as prompts. 

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

  • AI Prompt Engineering: Definition and Importance
  • Improving Data Quality with Prompt Engineering
  • Benefits of AI Prompt Engineering in Data Engineering
  • Example of AI Prompt Engineering for Data Quality Improvement
  • Take the Next Step Towards Becoming an AI Prompt Engineer with Interview Kickstart
  • FAQs on AI prompt Engineering in Data Engineering

AI Prompt Engineering: Definition and Importance

AI prompt engineering is a process of crafting precise and effective prompts that can help LLM models generate desired outcomes with the help of a set of instructions as input. Prompt engineering is a practice that acts as a bridge between intentional human prompts and generative AI model outputs. 

AI prompt techniques include refining and compiling a set of instructions for the generative AI models, which are already being trained on a wide range of datasets. The prompts help in data engineering by making the models develop a better understanding of contextual data that generates better responses.

Importance of AI Prompt Engineering in Data Engineering

The main task for any AI prompt engineer is to develop a set of instructions in the form of diverse prompts to help the machine learning models process and enhance the quality of data. In the AI world, prompt engineering helps to train the models on the basis of different input prompts, which then generate the desired output. It is a problem-solving skill in the field of data engineering, where prompts help optimize the AI model’s performance with refined interactions between human intent and AI. It acts as a medium to connect the world of data processing with human queries.

Improving Data Quality with Prompt Engineering

With the growing amount of data every day and the new large language model being revealed every few weeks, data engineering, data processing, and many other data-related fields are finding new ways to integrate prompt engineering to benefit them.

Data Completeness

With the plethora of sources to collect data and organize it and millions of rows and columns to deal with, sometimes not all the values are filled, and not all the data blocks have data inside of them. In order to deal with incomplete datasets, which can hinder analysis and decision-making, validation-checking scripts can be generated by AI to prompt engineers to identify the missing values. Imputation scripts can also be perfected with the help of the correct prompt and the use of capable code-generation models.

Data Accuracy

Data, even when it’s complete, can have multiple sources, and they can be valid, maybe, or not reliable. If a data processing pipeline is automated to generate some business-related content or if processed data is meant to be used for crucial predictions, data inaccuracy can be harmful. 

Inaccurate data can be fixed by checker programs generated with the help of code assistance prompts or code completion prompts, anomaly detection functions, validation checks, and logic against sources that can be written with proper prompt engineering, which helps engineers achieve data quality assurance.

Consistency of Data

For a singular use case or a project, data fields can belong to the parent category but can have different formats. For example, a temperature column in collected data can have values in multiple formats, like Fahrenheit, Celsius, Kelvin, etc. Non-standardized values can lead to difficulties in training the model. AI prompt engineers can implement prompts to be sent with data to the models to get the output of coding scripts that standardize data format, units, and definitions across the data set. Functions to implement data normalization techniques to ensure consistency throughout the whole data lifecycle can also be improved from a contextual prompt.

Timeliness of Data

On various artificial intelligence real-time projects or data processing automated workflows where new data needs to be fed and converted into training data, or, in other words, projects where data freshness is required, AI prompt engineers can define prompts that, if sent with data to the model, can check the freshness or timeliness of the data. 

In the case where synthetic data needs to be generated for some time, a specific time range can be specified within the prompt to keep the output response in check. Time-based filters can also be sent in the context of the prompt while cleaning the data. Data pipelines that feed the most recent data to the model can also be programmed with prompt-assisted coding.

Setting Up Data Sources and Storage

Before cleaning and implementing AI and data processing technologies, identifying the correct vendors of data and the reliable platforms to store that data so that the retrieval, saving, and updating chain works effortlessly and seamlessly is equally important as any other step. Crafting prompts that optimize the query and feeding indexing context with them can solve many efficiency problems in SQL/NoSQL-based databases. Always ensure that prompts align with the scheme of the database to avoid any inconsistency. Prompts should be written to generate programs that scrape data ethically from multiple data sources and maintain diversity in data vendors. Important context information like regulations and examples of needed data should be added to the prompt if code generation is being done for data collection.

In the Creation of New Data

The performance of large AI models in the tasks of regression, prediction, sentiment analysis, entity recognition, etc., is purely based on the quality of the data and the detection of important variables in the data. Sometimes, these important variables might not be present initially in the cleaned data, and they need to be created by performing operations on existing variables. Experimentation variables can be generated with prompts, which can result in various unique variables to test in models. 

At the same time, generation text data parameters should be sent with the prompt to make the model output a response with controlled length and creativity. AI prompt engineers can also adjust prompts to refine the quality of the generated data.

For the Security of Data

It is an odd term when talking about data quality improvement. Still, even after all the prompting and betterment of data, if it is not secure and easily accessible, then that is not good news for the end product or data consumer. AI prompt engineers should always avoid exposing confidential data in prompts and ensure that no credential is passed with the prompt, even if the model is in-house.

End-to-end data encryption scripts can be upgraded with passing techniques with the prompt for the model to learn from the context and generate more secure scripts. When generating code for data quality, it should be checked that access control is not being passed to the wrong user, as multiple data engineers can use the code.

Benefits of AI Prompt Engineering in Data Engineering

Prompt engineering plays a significant role in developing optimized outputs in large language models. An AI prompt engineer prepares and understands the data for prompting and then evaluates and develops the data according to the technical requirements to streamline the workflow.

Flow Chart of prompt-based language models

Source: MDPI

  • Human-AI Interaction

AI prompt engineering plays a pivotal role in developing the collaboration between responsive AI models and humans. In data engineering, the professionals in the field, like data engineers, software engineers, and AI prompt engineers, effectively interact with AI models and trigger the required outputs with the help of detailed output indicator prompts.

  • Data Analyzation Process

Prompt engineering has proven to be an important tool for data analysis, visualization, and insights. An AI prompt engineer or data engineer can utilize prompts to do hypothesis testing on different datasets to get valuable insights in no time. Prompt engineering is also helpful in data visualization, as the experts can craft an interactive prompt and command the AI model to visualize charts and line graphs.

  • Iteration and Evaluation

The prompt engineering process involves refining prompts based on the business requirements to generate better results. It involves generating and drafting the initial input prompt to provide data quality assurance to the AI models. 

The process of data evaluation through prompts involves testing various prompts on AI models. It includes evaluating if the generated output is in alignment with the expected output and tailoring the prompts on the basis of the evaluation to make sure the model delivers high-quality outputs.

  • Fine-tuning the AI Model

After training the different datasets with prompts, the AI models undergo the fine-tuning process to ensure the data quality in regard to the particular task. The fine-tuning process for any model is to train them on diverse parameters according to domain-specific tasks, which boosts the understanding of generative AI models and enhances the outputs of the prompt in question.

Example of AI Prompt Engineering for Data Quality Improvement

Data quality improvement is big for all the machine learning industry engineers and researchers,  but prompt engineering is here to save the data. Let's look at how AI-assisted coding works.

  1. While assessing the data or while creating it, the outcome and operation to carry on the data should be clear, the task should be understood, and prompt creation should be carried out.
  2. With a more precise and informative prompt that considers all the edge cases, The output can exceed and match the expectations of AI prompt engineers and data engineers.
  3. Let us write a prompt for data analysis of fetched data:

Given the following Data Records,  Play the Role of a Data engineer, and please provide the code to drop the row if there's a missing variable.


Sell "List" "Living" "Rooms" "Beds" "Baths" "Age" "Acres" "Taxes"

142 160 28 10 5 3 60 0.28 3167

175 180 18 8 4 1 12 0.43

129 132 13 6 3 1 41 0.33 1471

138 140 17 7 3 1 22 0.46 3204

232 240 25 8 4 3 5 2.05 3613

  1. The response is ready to use in any program or script.
Data improvement with the help of AI prompt engineering
  1. More functionality can be added in prompts like data filters, cleaning filters, etc. 

“Please write the code to add the functionality to remove the rows if 'taxes' are less than 1500.”

Enhanced output with more functionalities with the help of AI prompt engineering

Take the Next Step Towards Becoming an AI Prompt Engineer with Interview Kickstart

Prompt engineering is groundbreaking in the world of AI as it takes generative AI models to the next level with creative input prompts that generate optimized outputs. The role of an AI prompt engineer is to make sure the data quality remains the same after the generated response and the data processing becomes smoother.

Alongside the expansion in AI, many individuals who are starting their journey to become data engineers or professionals in the field can elevate their skills to the next level with a machine learning interview course offered by Interview Kickstart.

Crack your next big interview with the help of reputable industry experts. Join the FREE webinar today and embark on your journey towards excellence.

FAQs on AI prompt Engineering in Data Engineering

Q1. Does one need to know coding to be a prompt engineer?

Suppose you want to become an AI prompt engineer. In that case, you need to have a basic understanding of programming languages and coding, alongside the knowledge of natural language processing, machine learning algorithms, and large language models.

Q2. Does AI prompt engineering have a future?

With the continuous growth of AI models all over the world, the future of prompt engineering may impose more refined and effective ways to communicate with generative models like ChatGPT. Prompt engineering is already in high demand and is expected to have more opportunities in the future.

Q3. Is AI prompt engineering easy to learn?

Anyone can learn and become a prompt engineer with perseverance, hard work, and a love for machine learning and AI. Prompt engineering has its trajectory curve of learning, where you can learn and hone your skills with the right guidance and practice.

Q4. How does a prompt in AI work?

AI prompts are the set of input instructions provided to any machine learning or AI data model and help generate correct outputs with the given instructions or prompts.

Q5. What are some applications of AI prompt engineering?

Some of the use cases and applications of prompt engineering include text classification and summarization, answering queries, evaluation of outputs, etc.

Posted on 
January 5, 2024

Abhishek Som

Senior Content Specialist at Interview Kickstart

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