Text Dataset Creation
Building AI Language Skills

For simple classification: 500-2000 examples total (balanced across classes). For Q&A or chatbots: 1000-5000 pairs minimum. For instruction tuning (ChatGPT style): 10,000+ is ideal but you can start with 1000. Modern models with transfer learning can work with less, but more data = better results always! Focus on quality over quantity.

Yes, but be careful! AI-generated data can have biases and hallucinations. Best practice: use ChatGPT to generate initial examples, then manually review and edit each one. Mix AI-generated with human-written examples. Never use 100% AI-generated data without review - garbage in, garbage out! Always fact-check AI-generated content.

Match your use case! Customer service bot = casual friendly language. Legal/medical AI = formal professional text. Best approach: include BOTH styles so AI can adapt. Real-world users communicate in many ways, so train on variety! Include different writing styles, formality levels, and communication patterns that your users will actually use.

Vary the length! Include short (1 sentence), medium (2-3 sentences), and long (paragraph) examples. For classification: sentences are fine. For Q&A: 2-5 sentence answers work well. For chatbots: aim for conversational length (like how you'd actually reply). Avoid extremes - not one word, not 10 paragraphs. Diversity in length helps AI handle different input types.

CSV is simpler for beginners and works great for basic classification or Q&A. JSON is better for complex structures (multi-turn conversations, nested data, metadata). Start with CSV in Google Sheets, move to JSON when you need more structure. Most AI tools accept both formats anyway! JSON also supports additional fields like confidence scores, timestamps, and author information.

Include diverse perspectives, balance positive/negative examples, represent different demographics, avoid stereotypes. Use inclusive language, include various cultural contexts, ensure gender and racial diversity in examples. Have multiple people review data for unconscious biases. Use tools like Perspective API to detect toxic content. Diverse data creates more robust and fair AI models.

Write original content (best option), use public domain works (Project Gutenberg), Creative Commons licensed content, government documents, Wikipedia (with attribution), academic papers with open access, Reddit API (public posts), Twitter API (public tweets). Always check licensing terms. For commercial use, ensure all content has appropriate permissions. Original content is always safest.

Separate datasets by language for best results, or use multilingual models. Include language identification labels. For each language: ensure consistent quality, native speakers for review, cultural context awareness. Start with one language, expand to others. Balance dataset sizes across languages. Consider using translation tools but verify accuracy. Different languages have different grammar and cultural nuances.

Instruction tuning teaches AI to follow commands (instruction-response pairs). Fine-tuning adapts pre-trained models to specific domains or tasks. Instruction tuning creates versatile assistants that handle diverse requests. Fine-tuning creates specialists for specific tasks (medical diagnosis, legal analysis). For general-purpose chatbots, use instruction tuning. For domain-specific tasks, use fine-tuning. Often best to combine both approaches.

Clear, specific instructions with detailed responses. Include variety: creative writing, analysis, coding, math, explanations. Responses should be helpful, accurate, and well-structured. Use proper formatting, examples, and step-by-step explanations. Avoid ambiguity in instructions. Test instructions with multiple people to ensure clarity. Quality responses directly impact AI performance and user experience.

Establish clear content guidelines, use content filtering tools, have multiple reviewers for sensitive content. Include examples of appropriate responses to sensitive topics. Implement safety checks and content moderation in training data. Consider age-appropriate content, trigger warnings, and helpful resource suggestions. Balance between being helpful and maintaining safety. Regular review and update of content policies as needed.

Too short responses, no language variety, copying internet text directly, incorrect facts, biased data, inconsistent formatting, poor quality control, not testing with target users, ignoring edge cases, and not documenting data sources. Always fact-check, maintain variety, ensure quality, and test your dataset with real users before training large models.