As we dive into the world of cutting-edge data science, few individuals stand out like Dominic Jainy, an IT professional with deep expertise in artificial intelligence, machine learning, and blockchain. With a passion for leveraging these technologies to transform industries, Dominic has become a thought leader in advanced prompt engineering—a skill rapidly gaining traction in data science workflows. In this interview, we explore how prompt engineering is reshaping the field, from optimizing large language models (LLMs) for feature engineering to streamlining model selection and evaluation. We also delve into practical strategies for crafting effective prompts, balancing cost with quality, and applying these techniques to real-world data science challenges.
How would you describe prompt engineering, and why do you think it’s becoming a critical skill for data scientists today?
Prompt engineering is the art and science of designing inputs for large language models to get the most accurate, relevant, and useful outputs. It’s about understanding how to communicate with these models effectively—defining roles, setting tasks, and providing context. Its importance in data science is growing because LLMs can accelerate so many parts of our workflow, from brainstorming features to writing code for pipelines. As these tools become more integrated into our daily tasks, knowing how to craft precise prompts isn’t just a nice-to-have; it’s becoming essential to stay competitive and efficient.
What do you believe are the core elements of a well-structured prompt when working with LLMs?
A high-quality prompt typically has a few key components. First, you define the role and task clearly—like telling the model it’s a senior data scientist tasked with feature engineering. Then, context and constraints are critical; you need to provide details about the data type, desired output format, or any specific limitations. Including examples or tests also helps guide the model toward the expected result. Lastly, I often add an evaluation hook, asking the model to explain its reasoning or rate its confidence. Together, these elements ensure the output is targeted and usable.
Can you share some practical strategies for crafting effective prompts specifically for data science projects?
Absolutely. One strategy is using clean delimiters, like double hashtags or triple backticks, to separate sections of the prompt—this makes it scannable for both the model and the user. Another tip is to always place instructions before data; this helps the model focus on the task first. Also, be specific—don’t just ask for code, ask for a Python list or valid SQL. Finally, adjust the temperature setting of the LLM based on the task. For precise outputs like code generation, keep it low, around 0.3 or less. For creative tasks like brainstorming features, bump it up to encourage diverse ideas.
How do you approach balancing cost and quality when using LLMs for prompt engineering in your projects?
Balancing cost and quality is a real concern with LLMs, especially for larger projects. My approach is to use cheaper models for initial brainstorming or rough drafts—say, generating feature ideas or basic code snippets. Then, I switch to a premium model for refining and polishing the final output. This tiered strategy saves money without sacrificing quality. It’s also about being smart with token usage; I make sure prompts are concise yet detailed enough to avoid unnecessary iterations that rack up costs.
In what ways can LLMs support feature engineering across different types of data, like text or time-series?
LLMs are incredibly versatile for feature engineering. For text data, I use prompts to brainstorm semantic or linguistic features, like sentiment scores or key phrases, which can be directly plugged into predictive models. With time-series data, I might prompt an LLM for decomposition into trends and seasonal components, saving hours of manual work. Tools and frameworks like LLM-FE for tabular data are also game-changers—they use the model as an evolutionary optimizer to iteratively propose and refine features. The key is tailoring the prompt to the data type and validating outputs before integration.
How do you see LLMs contributing to model selection and pipeline building in machine learning workflows?
LLMs are a huge time-saver here. I can describe my dataset and target metric in a prompt, and the model will rank potential algorithms—like suggesting top models from scikit-learn—and even generate pipeline code. It can propose hyperparameter grids for tuning as well. Beyond that, I often ask for explainability, like why a certain model was ranked highest or for feature importance metrics post-training. This transparency helps me trust the recommendations and speeds up the entire process from selection to deployment.
What challenges have you encountered with prompt engineering, and how do you troubleshoot issues like hallucinated outputs or inconsistent results?
One common challenge is hallucination—where the model invents features or uses non-existent columns. I tackle this by embedding schema details and validation steps in the prompt. Another issue is overly creative outputs, like flaky code for pipelines; setting library limits and including test snippets helps. For inconsistent scoring in evaluations, I keep the temperature at zero and log prompt versions to track changes. These fixes ensure reliability, though it often takes some trial and error to get the balance right.
Looking ahead, what’s your forecast for the role of prompt engineering in the future of data science and machine learning?
I see prompt engineering becoming a foundational skill in data science, much like programming or statistics are today. As LLMs grow more powerful and integrated into tools, the ability to design effective prompts will directly impact productivity and innovation. I expect more research to focus on automating and optimizing prompts—think frameworks that self-adjust based on results. Ultimately, it’s about making AI a true partner in our workflows, and prompt engineering will be the bridge to that future.