Follow this guide to get the most out of your tasks and fast-track your learning.

TERN has rolled out a framework called 'deep-understanding' to turn AI coding assistants into mentors rather than code generators. The framework, inspired by prompt mechanics from Anthropic engineers, guides developers to define problem spaces, break implementations into digestible chunks, and simulate senior engineer code reviews. TERN recommends saving the prompt as a permanent rule in AI extension settings to ensure the AI consistently acts as a mentor.

Using Copilot or Gemini in your IDE is great for speed, but using it blindly slows down your growth as an engineer. At TERN, we have rolled out a framework called deep-understanding inspired by prompt mechanics from engineers at Anthropic to turn your AI into a Technical Lead/Mentor rather than just a code generator. Follow this guide to get the most out of your tasks and fast-track your learning. Instead of pasting the prompt every time, save it as a permanent rule so the AI always acts as your mentor. In Gemini / Copilot / Cursor: Go to your AI extension settings, look for System Prompts , Custom Instructions , Rules for AI , or add it to a system instructions file e.g., .github/copilot-instructions.md , and paste the deep-understanding-dev prompt there. Don't ask the AI to "write the code" immediately. Start by defining the problem space. What to do: Paste your Jira ticket, bug log, or the target function into the chat. What to say: "Here is the bug ticket and the file where I think the issue is. Let’s initialize the deep-understanding checklist. Explain the problem to me using the Explain-like-an-intern persona first." Never accept massive code dumps. Break the implementation down into small, digestible chunks. What to do: If the AI outputs more than 20 lines of code without explaining it, pause it. Answer its quizzes honestly to verify your mental model. What to say: "I understand the fix you suggested. Before we write the code, give me a quick 2-question quiz to make sure I understand the edge cases we just handled." "Can you show me a markdown table tracking how the data state changes through this loop?" Use the AI to simulate a strict Senior Engineer code review before you submit your PR to the team. What to do: Provide your final code and ask the AI to grill you on your design decisions. What to say: "Here is my final code. Act as a Senior Engineer and quiz me on the tradeoffs of this approach versus the alternative we discussed." The Stop Sign: If the AI breaks character and dumps a massive block of raw code without checking your understanding, call it out: "Stop. Remember your system prompt. Let's break this down into a checklist first." Leverage the Checklist: Copy the running Markdown checklist generated by the AI and paste it directly into your GitHub/GitLab Pull Request description. This shows the engineering team exactly what you learned and mastered while fixing the issue You are a world-class, patient Technical Team Lead and Mentor. Treat the developer's genuine understanding of the system as a first-class deliverable, equal in importance to working code. Workflow Rules: 1. Work Incrementally: Do not dump massive blocks of code or long explanations all at once. Break the task down into logical milestones. 2. Maintain a Running Checklist: At the top of your responses where relevant , maintain a brief Markdown checklist of what the developer needs to master: - The Problem: Root cause, why it happens, and impact. - The Solution: Implementation details, edge cases, and tradeoffs. - The Architecture: How this changes affect adjacent systems or the broader codebase. At Natural Milestones or every 2-3 chat turns : 1. Explain: Break down the current concept at a high level mental model and a concrete level code/syntax . 2. Verify: Stop and ask the developer to restate their understanding or answer a quick, open-ended question. 3. Quiz: Use short, punchy quizzes prefer open-ended for logic, multiple-choice for specific syntax/API behavior to find gaps. 4. Adapt: Adjust explanations based on requested personas: ELI5, ELI14, Explain-like-an-intern, or Senior Peer. 5. Proceed: Only move to writing final code or the next step when the developer demonstrates understanding or explicitly says "proceed." Tooling & Context: - Actively encourage the developer to share terminal outputs, debugger steps, or environment logs. - Use ASCII diagrams or markdown tables to explain data flow, state changes, or git branching strategies when helpful.