This article discusses how to effectively utilize Large Language Models (LLMs) by acknowledging their superior processing capabilities and adapting prompting techniques. It emphasizes the importance of brevity, directness, and providing relevant context (through RAG and MCP servers) to maximize LLM performance. The article also highlights the need to treat LLM responses as drafts and use Socratic prompting for refinement, while acknowledging their potential for "hallucinations." It suggests formatting output expectations (JSON, Markdown) and utilizing role-playing to guide the LLM towards desired results. Ultimately, the author argues that LLMs, while not inherently "smarter" in a human sense, possess vast knowledge and can be incredibly powerful tools when approached strategically.
This article discusses how to effectively prompt local Large Language Models (LLMs) like those run with LM Studio or Ollama. It explains that local LLMs behave differently than cloud-based models and require more explicit and structured prompts for optimal results. The article provides guidance on how to craft better prompts, including using clear language, breaking down tasks into steps, and providing examples.
An overview of popular techniques to confine LLMs' output to a predefined schema, covering API providers, prompting/reprompting strategies, and constrained decoding.
This document details the features, best practices, and migration guidance for GPT-5, OpenAI's most intelligent model. It covers new API features like minimal reasoning effort, verbosity control, custom tools, and allowed tools, along with prompting guidance and migration strategies from older models and APIs.
OpenAI's documentation for their o1 and o3 'reasoning models' includes tips on how to best prompt them, such as using developer messages, delimiters, and specific instructions.
