Prompt engineering in 2026 is less about clever tricks and more about systematic evaluation. The field has matured: most published 'techniques' have 2-5 percentage point effects on benchmarks, but the real gains come from having a test suite and measuring systematically rather than guessing.
System prompt fundamentals: be specific about persona, output format, and constraints. 'You are a helpful assistant' performs worse than 'You are a senior software engineer specializing in Python. Respond with code examples in Python 3.12. When you're unsure, say so explicitly.' Concrete role description, concrete output expectations, explicit handling of uncertainty — these three instructions consistently improve output quality.
Chain-of-thought (CoT) prompting adds 5-20% accuracy on multi-step reasoning tasks. The classic form: add 'Think step by step before answering' or show an example with reasoning in the few-shot examples. For production, extended thinking (Claude 3.7) or OpenAI's o3 models provide automatic CoT with better results than manual prompting. Use CoT for math, logic, multi-step planning — not for classification or extraction tasks where it adds latency without benefit.
Structured output is the most underused technique. Asking GPT-4o or Claude to output valid JSON with a schema produces 95%+ valid JSON without extra parsing. OpenAI's Structured Outputs feature (available via response_format) enforces the schema at the generation level — zero invalid JSON, even for complex nested structures. Anthropic's tool use (function calling) achieves the same. Always use structured output for any application that parses LLM responses programmatically.
Few-shot examples shift the model toward your specific distribution faster than any amount of instruction text. Three to five examples of the exact input-output pattern you want consistently outperform verbose zero-shot instructions. For extraction tasks, show the model examples of edge cases it's likely to see — the model infers the rules from examples more reliably than from descriptions.
Evaluation is where most teams underinvest. Build an eval dataset of 50-200 input/expected output pairs before you start prompt iteration. Run every prompt change against the eval set. Without this, you're optimizing for the last thing you tried, not for actual improvement. LLM-as-judge (using a strong model to score outputs) scales this evaluation cheaply — $5-20 of API calls to evaluate 100 examples.