Latest

Complete guide to keyboard shortcuts across major AI chatbots like ChatGPT, Claude, and Perplexity. Enter to submit and Shift + Enter for new lines dominates the landscape.

Learn how to transform AI-generated tests from superficial to robust by mastering prompt engineering, planning workflows, and custom Claude Code commands for reliable test coverage.

Learn how to install Playwright globally on macOS and connect it to Claude Code via MCP for seamless browser automation without duplicate downloads.

This article outlines optimal fixed-size chunking strategies for OpenAI-based Retrieval-Augmented Generation (RAG) systems using SaaS product documentation. It categorizes common documentation types—API references, tutorials, onboarding guides, concept overviews, and FAQs—and recommends chunk sizes, overlap, and expected chunk counts for each, all within OpenAI's token constraints. Smaller chunks enhance precision for structured docs like APIs and FAQs, while larger chunks preserve context in tutorials and conceptual guides. Strategic overlap ensures continuity without redundancy. The study also offers guidance on improving documentation structure to enhance AI retrieval accuracy, ensuring effective support chatbots and file search experiences built on OpenAI models.

When using LLMs with external tools, the tool_choice parameter determines if and how tools are called. Options include "none" (disable tools), "auto" (model decides), "required" (at least one tool must be used), and forcing a specific tool. Each has distinct behaviors and suits different use cases—from flexible chatbots to compliance-critical systems. Proper use requires clear system instructions and well-defined tool descriptions. Additionally, enabling parallel_tool_calls allows multiple tools to be invoked in one step. Choosing the right configuration ensures accurate, efficient, and context-aware tool use, making it essential for building reliable AI-driven workflows and assistants.