Prompt engineering

Structuring text as input to generative artificial intelligence

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3 news mentions · 👍 0 likes · 👎 0 dislikes

📌 Topics

Artificial Intelligence (2)
Scientific Computing (1)
Plasma Physics (1)
Knowledge Retrieval (1)
Computational Efficiency (1)

🏷️ Keywords

Plasma GraphRAG (1) · gyrokinetic simulations (1) · parameter selection (1) · Graph RAG (1) · large language models (1) · plasma physics (1) · arXiv (1) · scientific AI (1) · GraphRAG (1) · Large Language Models (1) · Retrieval-Augmented Generation (1) · HyperNode Expansion (1) · Logical Path-Guided Evidence Localization (1) · Multi-hop Reasoning (1) · Computational Efficiency (1) · Knowledge Integrity (1)

📖 Key Information

Prompt engineering is the process of structuring natural language inputs (known as prompts) to produce specified outputs from a generative artificial intelligence (GenAI) model. Context engineering is the related area of software engineering that focuses on the management of non-prompt contexts supplied to the GenAI model, such as metadata, API tools, and tokens. During the 2020s AI boom, prompt engineering became regarded as an important business capability across corporations and industries.

📰 Related News (3)

🇺🇸 Plasma GraphRAG: Physics-Grounded Parameter Selection for Gyrokinetic Simulations (2026-04-09)
arXiv:2604.06279v1 Announce Type: cross Abstract: Accurate parameter selection is fundamental to gyrokinetic plasma simulations, yet current practice...
🇺🇸 UnWeaving the knots of GraphRAG -- turns out VectorRAG is almost enough (2026-04-01)
arXiv:2603.29875v1 Announce Type: cross Abstract: One of the key problems in Retrieval-augmented generation (RAG) systems is that chunk-based retriev...
🇺🇸 HELP: HyperNode Expansion and Logical Path-Guided Evidence Localization for Accurate and Efficient GraphRAG (2026-02-25)
arXiv:2602.20926v1 Announce Type: new Abstract: Large Language Models (LLMs) often struggle with inherent knowledge boundaries and hallucinations, li...

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Large language model · 1 shared articles