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Large Language Models

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Prompt Engineering

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Prompt Engineering

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Prompt Engineering

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Prompt Engineering

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Shot Prompting Techniques | Zero Shot, One Shot & Few Shot Prompting.

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You Need to Start Prompting Like This!

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Socrates Would Have LOVED ChatGPT

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Articles (63)

ArXiv cs.AI 🧠 Large Language Models 📄 Paper ⚡ AI Lesson 3d ago

Evaluation of Embedding-Based and Generative Methods for LLM-Driven Document Classification: Opportunities and Challenges

arXiv:2604.04997v1 Announce Type: cross Abstract: This work presents a comparative analysis of embedding-based and generative models for classifying geoscience

ArXiv cs.AI 🧠 Large Language Models 📄 Paper ⚡ AI Lesson 3d ago

A State-Update Prompting Strategy for Efficient and Robust Multi-turn Dialogue

arXiv:2509.17766v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) struggle with information forgetting and inefficiency in long-horizon, mu

ArXiv cs.AI 🧠 Large Language Models 📄 Paper ⚡ AI Lesson 3d ago

Unlocking the Potential of Diffusion Language Models through Template Infilling

arXiv:2510.13870v2 Announce Type: replace-cross Abstract: Diffusion Language Models (DLMs) have emerged as a promising alternative to Autoregressive Language Mo

Hackernoon 🧠 Large Language Models ⚡ AI Lesson 3d ago

Why "Build an AI Agent" Is the Wrong Starting Point for AI Systems

The industry is obsessed with agents and prompting. That focus is not wrong—but it is incomplete. Real production systems require architecture, determinism, int

ArXiv cs.AI 🧠 Large Language Models 📄 Paper ⚡ AI Lesson 4d ago

Context Engineering: A Practitioner Methodology for Structured Human-AI Collaboration

arXiv:2604.04258v1 Announce Type: new Abstract: The quality of AI-generated output is often attributed to prompting technique, but extensive empirical observati

ArXiv cs.AI 🧠 Large Language Models 📄 Paper ⚡ AI Lesson 4d ago

Strengthening Human-Centric Chain-of-Thought Reasoning Integrity in LLMs via a Structured Prompt Framework

arXiv:2604.04852v1 Announce Type: cross Abstract: Chain-of-Thought (CoT) prompting has been used to enhance the reasoning capability of LLMs. However, its relia

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