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Prompt Engineering Best Practices: Textual Inference & Sentiment Analysis
Data Science   Latest   Machine Learning

Prompt Engineering Best Practices: Textual Inference & Sentiment Analysis

Author(s): Youssef Hosni

Originally published on Towards AI.

Prompt Engineering for Instruction – Tuned LLMs

LLMs offer a revolutionary approach by enabling the execution of various tasks with a single prompt, streamlining the traditional workflow that involves developing and deploying separate models for distinct objectives.

Through practical examples, the article illustrates the efficiency of LLMs in tasks such as sentiment analysis of product reviews, identification of emotions, and extraction of valuable information like product and company names from customer reviews.

The versatility of LLMs is further demonstrated as they seamlessly perform multiple tasks concurrently through unified prompts. The article also delves into more complex natural language processing tasks, including topic inference and indexing topics for news articles.

Overall, the transformative impact of prompt engineering with LLMs is highlighted, presenting an efficient and powerful tool for both seasoned machine learning developers and those entering the field.

Getting Started & Setting Working EnvironmentSentiment Analysis of Product ReviewIdentify EmotionsDoing Multiple Tasks at OnceExtract Product & Company Names From Customer ReviewsTopics InferringMake a News Alert for Certain Topics

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