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Prompt Engineering

Definition
AI-generated

Prompt engineering is the practice of designing inputs (instructions, examples, format constraints) to steer a language model toward reliable outputs without changing its weights—often using role prompts, few-shot demonstrations, or structured templates.

Topics
LLM and Agents
Related
Context Context Engineering Harness Engineering

Why it matters in GWAS

Clear prompts improve reproducibility when using LLMs for tasks such as harmonizing trait descriptions, formatting citations, or generating analysis checklists; versioning prompts alongside code is as important as versioning software.

Example usage

"The methods explicitly include Prompt Engineering to support interpretation of the main findings."

Context Context Engineering Harness Engineering Genome-Wide Association Study (GWAS) In-Context Learning Large Language Model (LLM) LLM Hallucination Prompt Prompt Learning System Prompt Retrieval-Augmented Generation (RAG)
Related-term network

Click any node to open that term page.

Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Liu P, et al. (2023). Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing. ACM Comput Surv.
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