Few-Shot Learning Prompt Optimization 2026: Deep Turkish Technical Guide — From GPT-3 to Modern LLMs

## 1. Introduction Few-Shot Learning teaches LLMs via examples in prompt. Brown et al. 2020 GPT-3 discovery. Foundation of modern prompt engineering. ## 2. Three Levels Zero-shot (0 examples), One-shot (1), Few-shot (2-32+). ## 3. 8 Selection Strategies Random, Similarity-based KATE, Diversity, Active Learning, Semantic Clustering, Coverage, Difficulty Curriculum, Dynamic Retrieval. ## 4. Optimum Count 3-5 sweet spot for most tasks. 1 minimum. 10+ diminishing returns. ## 5. Ordering Effects Lost in the middle — primacy + recency. Critical examples at start + end. ## 6. Anthropic XML Pattern Modern best practice for example structuring. ## 7. Production Dynamic Few-Shot Retrieval (RAG + Few-Shot hybrid) for scale. ## 8. Conclusion Few-Shot foundation technique, still valuable in 2026 for domain-specific + Turkish + structured output.