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JD Tech
JD Tech
Nov 12, 2024 · Artificial Intelligence

Prompt Engineering: Concepts, Evolution, Techniques, and JD Logistics Application

This article explains what Prompt Engineering is, traces its development from early NLP commands to modern adaptive and multimodal prompting techniques, describes various prompting strategies such as Zero‑shot, Few‑shot, Chain‑of‑Thought, Auto‑CoT, and showcases a JD Logistics case study using these methods to classify product types with code examples.

AI Prompt DesignChain-of-ThoughtPrompt Engineering
0 likes · 27 min read
Prompt Engineering: Concepts, Evolution, Techniques, and JD Logistics Application
ByteFE
ByteFE
Jun 15, 2023 · Artificial Intelligence

Effective Prompt Engineering: Techniques, Prompt Injection Prevention, Hallucination Mitigation, and Advanced Prompting Strategies

This article explains how to craft efficient prompts by combining clear instructions and questions, discusses prompt injection risks and mitigation with delimiters, addresses hallucinations, and introduces zero‑shot, few‑shot, and chain‑of‑thought prompting techniques for large language models.

Chain-of-ThoughtLLMPrompt Engineering
0 likes · 16 min read
Effective Prompt Engineering: Techniques, Prompt Injection Prevention, Hallucination Mitigation, and Advanced Prompting Strategies
Sohu Tech Products
Sohu Tech Products
Mar 22, 2023 · Artificial Intelligence

An Overview of Prompt Learning in Natural Language Processing

This article reviews the evolution of NLP training paradigms, explains why prompt learning is needed, defines its core concepts, and surveys major hard‑template and soft‑template methods such as PET, LM‑BFF, P‑tuning, and Prefix‑tuning, highlighting their advantages for few‑shot and zero‑shot scenarios.

NLPPrompt Tuningfew-shot
0 likes · 10 min read
An Overview of Prompt Learning in Natural Language Processing
Python Programming Learning Circle
Python Programming Learning Circle
Jul 24, 2021 · Artificial Intelligence

Building a Personal Recommendation System for Few Users with Dynamic Labels in Python

This article explains how to design and implement a lightweight Python recommendation system for personal use by replacing rating‑based similarity with label‑based scoring, handling sparse data, and dynamically updating user models with time‑limited label weights.

Pythoncollaborative filteringdynamic labels
0 likes · 7 min read
Building a Personal Recommendation System for Few Users with Dynamic Labels in Python