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Explore insights, guides, tutorials, and ideas across AI, technology, productivity, and other topics worth reading.
AI Agent Prompts: How to Write Better Instructions for Tool-Using AI Agents
Learn how to design instructions for tool-using AI agents, including goals, scope, tool policies, handoffs, state, verification, stop conditions, and evaluation.
Long-Context Prompting: How to Use Large Context Windows Without Losing Important Information
Learn how to design long-context prompts, organize documents and instructions, choose between direct context and RAG, manage growing conversations, and preserve important information across large AI context windows.
Prompt Caching: How to Reduce LLM Cost, Latency, and Repeated Context
Learn how to structure prompts for cache reuse, separate stable prefixes from dynamic inputs, measure cache hits, and compare current OpenAI, Claude, and Gemini caching behavior.
Video to Prompt: How to Reverse-Engineer Videos Into AI Prompts
Learn how to analyze a reference video’s subject, motion, camera, lighting, timing, style, audio, and shot structure, then reconstruct those observable signals into a reusable AI video-generation prompt
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.