#prompt engineering

'A practical walkthrough to run a GPT-style conversational agent locally using Hugging Face Transformers, complete with code, prompt patterns, and a demo.'

AI copywriters are transforming marketing by producing scalable, personalized content, but the real advantage comes from human and machine collaboration.

'Context engineering turns context into a core design layer for AI agents, focusing on JIT retrieval, memory strategies, and tool design to keep reasoning precise and efficient.'

'Avoid common pitfalls with uncensored AI video generators by writing clear prompts, focusing tone and emotion, iterating drafts, and adding human post-editing to achieve better results.'

'Learn how JSON prompting turns vague instructions into precise, machine-readable requests for LLMs, with Python examples comparing free-form and JSON outputs to show the gains in consistency and integration.'

'Microsoft introduced POML, a markup language that structures and modularizes LLM prompts with templates, styles, and SDKs to streamline prompt engineering.'

Discover how context engineering advances large language models beyond prompt engineering with innovative techniques, system architectures, and future research directions.

Discover how to use Mirascope to implement the Self-Refine technique with Large Language Models, enabling iterative improvement of AI-generated responses for enhanced accuracy.

Discover four key principles of prompt engineering that transform vague requests into precise AI outputs, boosting your productivity with ChatGPT, Google Gemini, and Claude.

New research demonstrates that inference-time prompting can effectively approximate fine-tuned transformer models, offering a resource-efficient approach to NLP tasks without retraining.

'Explore the evolution, architecture, and optimization techniques for API-calling AI agents, including practical workflows and examples for engineering teams.'

Google AI and University of Cambridge introduce MASS, a novel framework that optimizes multi-agent systems by jointly refining prompts and topologies, achieving superior performance across multiple AI benchmarks.

Meta introduces Llama Prompt Ops, a Python package that automates the conversion and optimization of prompts for Llama models, easing transition from proprietary LLMs and improving prompt performance.

This tutorial explores building a powerful question-answering system by integrating Tavily Search API, Chroma, Google Gemini LLMs, and LangChain, featuring hybrid retrieval, semantic caching, and advanced prompt engineering.

A recent study reveals that being polite to AI does not enhance the quality of its answers, as AI output deterioration depends on content tokens rather than courteous language.