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Prompt Engineering

The practice of designing and refining inputs to language models to elicit more accurate, useful, and consistent outputs.

Why it matters

Good prompts are the difference between a demo and a product. Even with great models, the quality of your instructions determines the quality of your output.

Core techniques

  • Zero-shot — ask the model directly with no examples.
  • Few-shot — include 2–5 examples of the desired input/output pattern.
  • Chain-of-thought — instruct the model to reason step by step before answering.
  • System prompts — set the model's persona, constraints, and output format upfront.

Beyond prompt tricks

Prompt engineering is evolving from artisanal prompt-writing into a more systematic discipline. Production systems use structured prompts with clear sections (context, instructions, constraints, output format), version-controlled prompt templates, and automated evaluation of prompt variants.

Diminishing returns

As models improve, elaborate prompting techniques matter less. The biggest gains come from providing the right context (via RAG or tool results) rather than clever wording. Focus on what information the model needs, not on magic phrases.

Related Terms

Large Language Model(LLM)— A neural network trained on massive text corpora that can understand and generate human language, typically with billions of parameters.Retrieval-Augmented Generation(RAG)— A technique that grounds a language model's output in external data by retrieving relevant documents before generating a response.

On the AI Radar

Chain-of-Thought— A prompting technique that guides LLMs through step-by-step intermediate reasoning before producing a final answer, significantly improving performance on complex tasks. Has evolved from a prompt engineering technique into a core architectural feature embedded in reasoning models.Prompt Caching— Stores and reuses previously computed key-value tensors from attention layers to avoid redundant computation on repeated prompt prefixes, delivering up to 90% cost reduction and 85% latency reduction. All three major providers now offer it with different implementations.
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