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Scenario-based tutorials with real screenshots, prompts, workflow templates and pitfalls.
Once your brand grows, you won't know who's trashing you. n8n periodically scrapes mentions across platforms, an LLM runs sentiment analysis (positive / negative / neutral + severity), negative ones alert instantly, and all are archived into a daily report. Includes a .json template and a sentiment-analysis prompt.
Taking content overseas or running a multilingual site means manually translating every post-grunt work. n8n watches source-doc updates, an LLM translates per a glossary, and writes to per-language dirs. Includes a .json template download and a companion translation prompt.
Don't just say "summarize the meeting." Beginner structured minutes / intermediate action-item table (owner / due date / dependencies) / expert follow-up tracking + risk warning-turn meetings into a trackable execution chain. The key is feeding the AI last meeting's action items beforehand.
Don't just say "translate this." Beginner literal (faithful) / intermediate elegance (fluent + style + glossary) / expert localization (cultural, audience, compliance adaptation). The key is a glossary-without one, inconsistent terminology is the biggest failure.
Daily posting runs on a pipeline, not inspiration. Five steps: topic -> dissect hits -> generate script -> storyboard & voiceover -> batch dedupe, with 4 copy-paste prompts (topic / dissection / script / dedup), 3 storyboard placeholders, and 7 pitfalls. AI mass-produces; humans own topic direction, fact-checking, and voiceover polish.
Don't believe "just wire up an API." Four-layer architecture (intent / knowledge / reply / backstop), a Coze workflow (intent classification + knowledge retrieval + persona constraints + human handoff), 3 copy-paste prompts, a 4-step grayscale rollout, and 7 pitfalls. Run shadow mode before scaling.
When open-source issues pile up, manual triage is a disaster. This n8n workflow listens to a GitHub webhook, LLM judges type (bug/feature/docs/question) and priority (P0-P3), calls GitHub API to label, then notifies a channel. With .json template, classification prompt, and three pitfalls.
30 minutes a day on daily reports is the lowest-ROI grunt work. This n8n workflow pulls Git/task-system data on schedule, LLM generates a structured report (done/in-progress/tomorrow/risks), auto-pushes to Feishu/DingTalk. With .json template download and companion prompt.
Reading alone is not learning. Beginner explains concepts in plain words + analogies, intermediate has AI probe your blind spots, expert generates 5 easy-to-hard quiz questions. Three steps to truly master a concept-the key is making AI probe and quiz.
Stop saying "check for bugs." Beginner quick bug localization (cite lines + minimal fix), intermediate full code review (red/yellow/green priorities), expert refactor advice (coupling/duplication/long functions)-key constraints pull AI from vague to concrete.