Blog
Explore insights, guides, tutorials, and ideas across AI, technology, productivity, and other topics worth reading.
How to Write Prompts for ChatGPT, Claude, Gemini, and Grok in 2026
Learn which prompt-engineering principles transfer across ChatGPT, Claude, Gemini, and Grok, which model-specific techniques need adaptation, and how to evaluate cross-model prompt portability.
Prompt Templates and Variables: How to Build Reusable AI Prompts
Learn how to turn one-off prompts into reusable prompt templates with variables, runtime context, validation, versioning, testing, and prompt-as-code practices.
Few-Shot Prompting: How to Use Examples for More Reliable AI Responses
Learn how few-shot prompting uses carefully chosen examples to teach AI models task patterns, output formats, decision boundaries, tone, and edge-case behavior without fine-tuning.
Structured Outputs: How to Make AI Return Reliable JSON and Schemas
Learn how structured outputs turn model responses into predictable, machine-readable contracts using JSON Schema, validation, tool calling, error handling, and provider-native schema enforcement.
Context Engineering: How to Give AI the Right Information at the Right Time
Learn how to design the information environment around an AI model by selecting, structuring, retrieving, compressing, and updating the instructions, memory, tools, state, and evidence it needs for each task.
System Prompts: How They Work and How to Write Better AI Instructions
Learn how system prompts shape AI behavior, how they differ across OpenAI, Claude, and Gemini, and how to design reliable instructions for roles, boundaries, tools, outputs, uncertainty, and evaluation.
Prompt Chaining: How to Build Better Multi-Step AI Workflows
Learn how to split complex AI tasks into focused prompt stages, pass structured outputs between steps, add validation gates, control context, prevent cascading errors, and design reusable multi-step AI workflows.
AI Prompt Evaluation: How to Test, Compare, and Improve Prompts
Learn a practical framework for AI prompt evaluation, from defining success criteria and building representative test cases to scoring outputs, comparing prompt variants, testing across models, and preventing prompt regressions.
Claude Code Dynamic Workflows: Examples, Ultracode, and Multi-Agent Orchestration
Explore Claude Code Dynamic Workflows, including agent(), pipeline(), Ultracode, /deep-research, workflow scripts, background runs, resumability, permissions, runtime limits, and reusable multi-agent orchestration patterns.