Chain-of-Thought, few-shot, system prompts, ReAct and Constitutional AI: the prompt engineering techniques that make the difference in production in 2026.
Direct instruction with no examples. Works well for simple tasks. Modern LLMs excel at zero-shot for 80% of common cases.
Provide 3-5 input/output examples in the prompt. Improves accuracy by 15-40% on structured tasks (extraction, classification). Standard for business templates.
Ask the model to "think step by step". Reduces reasoning errors by 40% on complex problems. Activate with: "Explain your reasoning step by step before answering."
Define role, output format, constraints and examples in the system prompt. Used by 94% of LLM production deployments (Anthropic Cookbook 2026).
Reasoning combined with tool calls. The model plans, executes tools (search, calculation, API), observes results and iterates. Foundation of modern AI agents.
Force output to JSON or a defined schema. Eliminates parsing errors. Available natively in OpenAI, Anthropic and Google APIs.
"Summarise this document", no length constraint, format, audience. Always specify expected format and target length.
"Don't be formal" is less effective than "Use a conversational tone, like talking to a colleague."
Beyond 7 constraints in one prompt, LLMs start ignoring some. Decompose into sub-tasks.
LLMs can generate syntactically valid JSON that is semantically wrong. Validate critical outputs with business rules.
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