Prompt Engineering

Users are growing tired of overly agreeable and inaccurate AI responses. Discover the common frustrations with LLMs like ChatGPT and the clever workarounds people are using to get better, more critical results.

Explore the key differences between Model Context Protocol (MCP) and RAG. Learn how MCP servers empower LLMs to perform actions and interact with live data, and discover practical use cases and best practices.

Frustrated with AI coding assistants like ChatGPT and Claude giving you bad code? Discover key strategies for improving their performance, from advanced prompting techniques to proper context management.

Developers share their real-world experiences and practical tips for using Claude Code and other AI assistants effectively, covering everything from prompt strategy and language choice to advanced tooling.

Developers discuss their real-world local LLM setups, sharing practical tools like Ollama, clever workflows for code explanation and automation, and a breakdown of the hardware vs. cloud subscription debate.

Feeling underwhelmed by AI's impact on your coding productivity? Discover the specific strategies and targeted use cases that developers are using to achieve real gains, moving from hype to helper.

Discover the debate on whether to be polite to your AI. Learn how tailoring your tone, from saying 'please' to being demanding, can dramatically improve your LLM's output.

Discover why the way we interact with LLMs—using decomposition, multi-perspective engagement, and a collaborative tone—may be more crucial than perfecting prompts.

Developers discuss the challenges of managing user context in LLM applications and the desire for an automated solution to make AI interactions more relevant. Explore current workarounds and the vision for a 'Segment for LLMs'.