Technique on English AI Terms Dictionary

Self-Consistency

Sat, 18 Jul 2026 10:14:51 +0000

Definition

Primarily used with Large Language Models (LLMs), this technique improves accuracy by generating several diverse responses to a prompt via sampling. Instead of relying on greedy decoding, it aggregates these outputs and applies majority voting to determine the most consistent result. This method effectively reduces hallucinations and enhances logical reasoning capabilities in complex tasks like mathematical problem-solving or code generation.

Summary

Self-consistency is a decoding strategy where multiple reasoning paths are sampled and the most frequent answer is selected as the final output.

Feature Engineering

Sat, 18 Jul 2026 09:57:52 +0000

Definition

Feature engineering is the art of leveraging domain expertise to transform raw data into features that better represent the underlying patterns to machine learning algorithms. This process includes creating new variables, combining existing ones, and selecting the most informative attributes. Effective feature engineering often leads to significant improvements in model accuracy and generalization, making it a critical step in the data science workflow.

Summary

The practice of using domain knowledge to create new features or modify existing ones to enhance the performance of machine learning models.

Feature Extraction

Sat, 18 Jul 2026 09:57:52 +0000

Definition

Feature extraction involves transforming raw data into a set of features that better represent the underlying problem to the predictive models, resulting in improved model accuracy. This technique reduces the number of random variables under consideration by obtaining a set of principal features. It is commonly used in image processing, signal analysis, and text mining to isolate relevant characteristics from complex datasets.

Summary

The process of deriving meaningful information from raw data to reduce dimensionality and improve machine learning model performance.

Few-Shot Prompting

Sat, 18 Jul 2026 09:40:59 +0000

Definition

This method leverages the in-context learning capabilities of large language models by providing a few illustrative examples directly in the prompt. Unlike fine-tuning, which requires updating model weights, few-shot prompting allows users to steer the model’s output format, tone, or logic dynamically. It is highly effective for tasks like classification, translation, or code generation where specific patterns need to be demonstrated to the model before generating the final response.