Basic prompting से परे, advanced techniques — few-shot, chain-of-thought, structured output, system prompts, और अन्य — complex tasks के लिए LLM results को काफी बेहतर बनाती हैं। उन्हें समझना LLMs से अधिकतम लाभ उठाने में मदद करता है।
Key advanced techniques
✓ FEW-SHOT → provide EXAMPLES of input/output in the prompt → the model follows the pattern
(great for specific formats/behaviors); ZERO-SHOT = no examples (just instructions)
✓ CHAIN-OF-THOUGHT (CoT) → ask the model to REASON step by step ('think step by step') →
improves complex reasoning/math (shows its work → more accurate)
✓ STRUCTURED OUTPUT → ask for a specific format (JSON, etc.) → reliable parsing for app
integration (often with schemas/tools)
✓ SYSTEM PROMPTS → set overall behavior/role/rules (the model's persistent instructions)
✓ ROLE/persona → 'You are an expert X' → frames responses
