Best Practices Articles
5 articles across AI Catchup's news, guides, tutorials, and comparisons.
All Best Practices articles
Prompting Claude Fable 5: What to Change and What to Delete
Prompt Claude Fable 5 with less, not more: brief instructions now beat enumerated rule lists, and old skills written for prior models can degrade output. Use effort as your main cost control, expect longer turns, ground progress claims in tool results, and configure fallback to Opus 4.8 for safeguard refusals.
Prompting Claude Opus 5: Trim the Verbosity, Delete the Verification
Claude Opus 5 needs opposite prompting from its predecessors: you prompt for conciseness because effort no longer controls visible length, and you delete verification and double-check instructions because the model already does both. Constrain scope on narrow tasks, cap subagent spawning for cost, and keep thinking enabled at low effort rather than disabling it.
Prompting GPT-5.6: Message Roles, Effort, and Agentic Prompts That Work
GPT-5.6 rewards precise, explicit prompts: structure developer messages as identity, instructions, examples, then context, keep stable content first for prompt caching, and pick reasoning effort deliberately (xhigh for complex multi-step work). Move saved prompt objects into code before OpenAI shuts down v1/prompts on November 30, 2026.
Claude Opus 4.7 Best Practices: How to Actually Get the Most Out of the Upgrade
Claude Opus 4.7 follows instructions more literally than 4.6, runs longer agentic tasks more reliably, and ships a new xhigh effort level. The practical effect: prompts that worked before may now produce surprises, and the workflows that earn the upgrade are the ones built around detailed plans, deliberate effort selection, auto mode with /fewer-permission-prompts, and explicit verification steps. Boris Cherny's day-of-launch tips are the honest playbook; this guide breaks them down with the workflows readers can apply this week.
Prompt Engineering in 2026: The Playbook That Works Across Claude and GPT
Prompt engineering in 2026 has two layers: cross-model fundamentals (clear direct instructions, motivated rules, 3-5 structured examples, XML boundaries, long documents before the question) and model-specific overrides that matter more than ever, because the newest models need instructions deleted as often as added. Start here, then apply the guide for your model.