Feature engineering enhances aspects of your knowledge base that we want to emphasize in a retrieval augmented generation RAG instance or other enterprise AI application. As part of our document preprocessing workflow, feature engineering signals what’s most relevant so that you get the very best performance from your AI implementation.
AI lecturer Fareesa Khan defines feature engineering as "a critical step in the machine learning pipeline, involving the creation, transformation, and selection of relevant data features to improve model performance."
In enterprise AI implementation, feature engineering often involves synthesizing complex operational data with metadata or other enrichments that orient AI to hierarchies or relationships that may not be immediately apparent.
As an analogy, a corporate organizational chart structures employees not just by name and title, but also by department, seniority, skill sets, and strategic importance. All of these are "features" of the employees, and the chart enriches or "engineers" those features to make them obvious and explicit.
Here’s why feature engineering is important:
Feature engineering uses techniques that transform raw data into more useful representations for AI:
Our feature engineering strategy is fairly straightforward: We identify issues in your knowledge base and create a tailored strategy to fix them. The roadmap includes any or all of the following interventions as needed:
Whether you're dealing with text data, images, time-series data, or categorical variables, feature engineering can improve the performance of your AI instance.
If you need assistance with data preprocessing and AI implementation, Talbot West will unlock the full potential of your data for a smooth and successful AI integration.
Here are some of the bottlenecks we often face when engineering features—and how we overcome them.
| Challenge | Solution |
|---|---|
Time-intensive process | Creating and selecting optimal features from raw data requires extensive manual exploration and testing of various combinations and transformations. |
Domain expertise requirement | Effective feature engineering requires a deep understanding of both the data and the specific domain to identify meaningful, pattern-capturing features. |
Manual process | Quality feature engineering of your corporate knowledge base is a very human-centric process. We’ve got it down to a science, with repeatable workflows and standardized processes. |
A law firm is implementing a RAG system to assist with case research. The large volume of legal documents makes it difficult to retrieve the most relevant information for specific cases. To address this, the firm applies advanced NLP techniques and generates legal-specific embeddings to enhance document retrieval.
An online retailer is using an LLM to automate customer support responses. The LLM struggles to provide accurate responses due to the diverse nature of customer inquiries. To improve this, the company implements feature engineering to extract key information from customer messages and provide structured context to the LLM.
An investment firm is using a RAG system to analyze quarterly financial reports. The system struggles to extract and compare financial metrics across different reports. To enhance performance, the firm develops custom feature extractors for financial data and creates standardized representations of financial metrics.
Examples of feature engineering include the following:
Feature engineering requires domain expertise, creativity, and a good understanding of how an AI system interprets your documentation. It involves identifying which features are most relevant to AI performance. All in all, it is an iterative and time-consuming process.
Feature engineering is a valuable skill in data science and machine learning. It requires technical knowledge of data manipulation and creativity to derive meaningful features that improve the quality of the responses you get from your AI instance. It requires understanding the problem domain, data types, and the underlying mechanics of machine learning algorithms.
Feature engineering remains highly relevant for enterprise AI integrations. We’d be happy to assess your use case and the state of your knowledge base and recommend whether feature engineering is necessary for you.
To master feature engineering, you should develop a strong foundation in data science, statistics, and domain-specific knowledge. Practice with different datasets to understand how different feature transformations affect model performance. Learn to use tools such as Python’s pandas, scikit-learn, and libraries specifically for feature engineering (such as Featuretools).
Also, we provide tailored feature engineering solutions so that your AI instance is built on the most relevant and impactful data for optimal performance.
Neural networks, particularly deep learning models, require less manual feature engineering than traditional models because they can automatically learn complex patterns and representations from raw data. Some preprocessing, such as normalization or data augmentation for images, is still necessary to enhance model training and performance.
Feature engineering is not the same as data engineering. Feature engineering focuses on transforming raw data into features that can improve AI performance. Data engineering involves the broader tasks of collecting, storing, processing, and managing data infrastructure.
Feature engineering is part of data preprocessing. Data preprocessing includes all steps taken to clean and prepare your knowledge base for AI ingestion, and feature engineering involves creating and transforming data features to improve accuracy and efficiency.
Principal Component Analysis (PCA) is part of feature engineering. PCA is a dimensionality reduction technique that transforms the original features into a smaller set of uncorrelated components, retaining as much variance as possible. This transformation reduces the feature space's complexity and improves model performance.
Talbot West bridges the gap between AI developers and the average executive who's swamped by the rapidity of change. You don't need to be up to speed with RAG, know how to write an AI corporate governance framework, or be able to explain transformer architecture. That's what Talbot West is for.
