Editorial Guidelines

1. Purpose & Scope

• Provide structured algorithm reasoning, not just final code.
• Teach problem-solving patterns (two pointers, DP, graph search, etc.).
• Highlight trade-offs between approaches.
• Offer multi-language parity where meaningful.

2. Content Structure (Minimum Standard)

1. Problem Restatement (Original): Paraphrased; never copy the source statement.
2. Intuition: What pattern or invariant unlocks the solution?
3. Progression: Brute force → refined → optimal.
4. Complexity: Time & space with justification.
5. Edge Cases: Explicit bullet list.
6. Common Mistakes: 2–5 pitfalls learners face.
7. Approach Variants: When alternative patterns are viable.
8. Annotated Code: Clean, minimal, consistent formatting.
9. Related Patterns / Problems: 3–6 internal links.
10. Real World Analogy / Application (when applicable).

3. AI-Assisted Content & Human Oversight

Our Content Creation Process:

1. Initial Generation: AI assists in creating first drafts of explanations, ensuring coverage of key concepts.
2. Human Review: Our technical team reviews every solution for accuracy, clarity, and educational value.
3. Code Verification: All code solutions are tested against actual test cases to ensure correctness.
4. Editorial Enhancement: Human editors add unique insights, interview tips, and real-world context.
5. Quality Scoring: Automated systems flag thin content for human improvement.

What Makes Our Content Unique:

• Expert-written author notes with personal insights and interview experiences
• Curated community tips from real interview experiences
• Human-verified complexity analysis with explanations
• Cross-referenced related problems and learning paths
• Regular updates based on user feedback and algorithm improvements

4. Originality & Attribution

• No verbatim copying of proprietary descriptions.
• AI-assisted drafts must be human-reviewed and materially improved.
• Use consistent variable naming aligned with explanation.
• Flag reused conceptual material for re-edit if >30% overlap.

5. Minimum Quality Gates

• Word count target: 600–1200 (skip code length).
• Readability: Aim Flesch score > 55 (internal metric planned).
• No orphan pages (≥2 internal inbound links).
• All JSON-LD fields populated where relevant (keywords, wordCount, dateModified).

6. Code Standards

• Prefer O(1) extra space where feasible; mention when trade-off chosen.
• Avoid micro-optimizations that reduce clarity unless justified.
• Consistent indentation & naming across languages.
• No commented-out dead code in final snippet.

7. Review Workflow

1. Draft authored or generated.
2. Human edit pass: originality, clarity, pedagogical value.
3. Automated audit (thin content script) passes.
4. Spot-check internal links & structured data validation.
5. Publish with timestamp; schedule periodic revalidation.

8. Maintenance & Updating

• Mark stale if untouched > 12 months or if better algorithm emerges.
• Batch refresh: complexity justifications, alternative methods, new related problems.
• Preserve canonical URL when updating to avoid fragmentation.

9. Disclaimers

Solutions are educational, unofficial, and not affiliated with problem sources or listed companies.