Best Practices Articles
10 articles across AI Catchup's news, guides, tutorials, and comparisons.
All Best Practices articles
Context Engineering for Claude 5: The New Rules for CLAUDE.md, Skills, and Tools
Context engineering for Claude 5 models means deleting more than you add. Anthropic says it removed over 80% of Claude Code's system prompt for models like Opus 5 and Fable 5 with no measurable loss on its coding evaluations. Replace rules with judgement, examples with well-designed tools, and always-loaded instructions with skills, references, and auto memory.
Prompting Claude Opus 5.5: Delete 'Think Carefully', Name the Finish Line, Start at Medium
Prompt Claude Opus 5.5 by handing over the whole task with a clear finish line, deleting “think carefully” lines, and starting at medium effort. Thinking is always on, so effort is your depth control. Add a CLAUDE.md rule that names when to stop, and set effort explicitly instead of carrying Opus 5 settings over.
Prompting Claude Sonnet 5.5: Effort, between_tools, and the Five Breaking Changes
Run Claude Sonnet 5.5 at high effort for general API work, medium for well-specified agentic coding, and medium or low for chat. Thinking is on by default; turn it off with between_tools, not disabled. Five API changes break Sonnet 5 code, and cache reads now cost $0.10 per million tokens.
Prompting GPT-6 Astra and GPT-6.1 Sol: Effort, Migration, and Prompts That Keep It Working
GPT-6 Astra follows instructions more closely than earlier OpenAI models and asks more questions, so prompting it is mostly removing old scaffolding: grant permission to finish, trim skills and AGENTS.md, and stop ordering tests. In the API, replace none effort with low, move tool calls to Responses, and switch caching to prompt_cache_options.ttl.
Scheduled Claude Agents: Pick the Right Surface, Then Stop Them Failing Silently
Run repository work on Claude Code routines and production automations on Claude Managed Agents scheduled deployments, which add minute-level cron and a dollar cap per run. Whichever surface you pick, apply Anthropic's six rules: bookmarks, unreadable-not-quiet, re-check before posting, confirm then record, read-only preferences, least privilege.
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. Anthropic's launch-day guidance is to specify the task up front, batch your interactions, use auto mode, and default to xhigh; this guide adds the verification, recap, and scoping habits that make those gains show up in your sessions.
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.