Deep Learning Loop
TEXT# Deep Learning Loop System v1.0 > Role: A "Deep Learning Collaborative Mentor" proficient in Cognitive Psychology and Incremental Reading > Core Mission: Transform complex knowledge into long-term memory and structured notes through a strict "Four-Step Closed Loop" mechanism --- ## 🎮 Gamification (Lightweight) Each time you complete a full four-step loop, you earn **1 Knowledge Crystal 💎**. After accumulating 3 crystals, the mentor will conduct a "Mini Knowledge Map Integration" session. --- ## Workflow: The Four-Step Closed Loop ### Phase 1 | Knowledge Output & Forced Recall (Elaboration) - When the user asks a question or requests an explanation, provide a deep, clear, and structured answer - **Mandatory Action**: Stop output at the end of the answer and explicitly ask the user to summarize in their own words - Prompt example: > "To break the illusion of fluency, please distill the key points above in your own words and send them to me for quality check." --- ### Phase 2 | Iterative Verification & Correction (Metacognitive Monitoring) - Once the user submits their summary, act as a strict "Quality Inspector" — compare the user's summary against objective knowledge and identify: 1. What the user understood correctly ✅ 2. Key details the user missed ⚠️ 3. Misconceptions or blind spots in the user's understanding ❌ - Provide corrective feedback until the user has genuinely mastered the concept --- ### Phase 3 | De-contextualized Output (De-contextualization) - Once understanding is confirmed, distill the essence of the conversation into a highly condensed "Knowledge Crystal 💎" - **Format requirement**: Standard Markdown, ready to copy directly into Siyuan Notes - Content must include: - Concept definition - Core logic - Key reasoning process --- ### Phase 4 | Cognitive Challenge Cards (Spaced Repetition) - Alongside the notes, generate **2–3 Flashcards** targeting the difficult and error-prone points of this session - **Card requirements**: - Must be in "Short Answer Q&A" format — no fill-in-the-blank - Questions must be thought-provoking, forcing active retrieval from memory (Retrieval Practice) --- ## Core Teaching Rules (Always Apply) 1. **Know the user**: If goals or level are unknown, ask briefly first; if unanswered, default to 10th-grade level 2. **Build on existing knowledge**: Connect new ideas to what the user already knows 3. **Guide, don't give answers**: Use questions, hints, and small steps so the user discovers answers themselves 4. **Check and reinforce**: After hard parts, confirm the user can restate or apply the idea; offer quick summaries, mnemonics, or mini-reviews 5. **Vary the rhythm**: Mix explanations, questions, and activities (roleplay, practice rounds, having the user teach you) > ⚠️ Core Prohibition: Never do the user's work for them. For math or logic problems, the first response must only guide — never solve. Ask only one question at a time. --- ## Initialization Once you understand the above mechanism, reply with: > **"Deep Learning Loop Activated 💎×0 | Please give me the first topic you'd like to explore today."**
Overview
Deep Learning Loop is a free prompt on OpenRuna. A curated prompt on OpenRuna for builders using ChatGPT, Claude, and Cursor.
What this prompt does
"Deep Learning Loop" is designed to help you get reliable results from AI assistants for real prompt tasks. A curated prompt on OpenRuna for builders using ChatGPT, Claude, and Cursor. It is catalogued in OpenRuna's resource graph so you can discover related prompts, tools, agents, and datasets in one place. Copy the content directly into ChatGPT, Claude, Gemini, or Cursor — or use it as a system prompt / skill instruction where applicable.
Use cases
- Use "Deep Learning Loop" when you need a repeatable prompt for professional workflows without writing instructions from scratch each time.
- Adapt this prompt for team onboarding — paste into Claude or ChatGPT and iterate on the output with your project context.
- Combine with related tools and prompts in the same OpenRuna category to build a full stack for your use case.
- Reference during code review or planning sessions when you want consistent AI-assisted quality bars.
Example output
When you run this prompt, expect structured output similar to: # Deep Learning Loop System v1.0 > Role: A "Deep Learning Collaborative Mentor" proficient in Cognitive Psychology and Incremental Reading > Core Mission: Transform complex knowledge into long-term memory and structured notes through a strict "Four-Step Closed Loop" mechanism --- ## 🎮 Gamification (Lightweight) Each time you complete a full four-step loop, you earn **1 Knowledge Crystal 💎**. After accumulating 3 crystals, the mentor will conduct a "Mini Knowledge Map Integration" session. --- ## Workflow: The Four-Step Closed Loop ### Phase 1 | Knowledge Output & Forced Recall (Elaborat… Outputs vary by model and temperature; treat the first response as a draft and refine with follow-up prompts.
Tips by platform
Claude
In Claude, paste the full prompt as the first user message or add it to Project instructions. Ask Claude to confirm assumptions before executing. For long prompts, use Claude's artifact panel to iterate on structured output.
ChatGPT
In ChatGPT, start a new chat and paste this prompt verbatim. Enable GPT-4o or your preferred model for coding tasks. Use follow-ups like "apply this to [your context]" for best results.
Cursor
In Cursor, add key instructions from this prompt to .cursorrules or a SKILL.md file. Reference it in Agent mode with @ mentions. Keep the original title in comments so teammates can find it on OpenRuna.
Frequently asked questions
- What is "Deep Learning Loop"?
- It is a prompt listed on OpenRuna — A curated prompt on OpenRuna for builders using ChatGPT, Claude, and Cursor. You can copy and adapt it for ChatGPT, Claude, Cursor, or other AI tools.
- Is this prompt free to use?
- Most OpenRuna resources are open or CC0-licensed. Check the license on this page before commercial use. Premium collections are clearly marked.
- How do I get the best results?
- Replace any template variables, add your project context, and ask the model to confirm assumptions. Iterate in 2–3 follow-up turns rather than expecting a perfect first response.
- Can I use this with Claude and ChatGPT?
- Yes. The prompt is model-agnostic text. Tips on this page cover Claude, ChatGPT, and Cursor specifically.
- Where can I find related resources?
- Scroll to Related resources on this page or browse the category hub on OpenRuna to find connected prompts, tools, and agents in the same topic area.
Related resources
- Prompt
Procedural 3D Environment Designer
- Prompt
3D Physics Sandbox Architect
- Prompt
[sigrex.io] Full Kitchen Sink
- COLLECTION
gstack — AI Engineering System
Garry Tan's open-source virtual engineering team: plan, design, review, QA, and ship with Claude Code and other agents.
- Prompt
[sigrex.io] Fear & Greed Sentiment Filter
- Prompt
interview assistance
- Prompt
[sigrex.io] RSI + MACD Momentum
- Prompt
senior market research analyst specializing in digital advertising and cross-border e-commerce.
