Prompt Engineering
The practice of designing and refining input instructions to elicit accurate, useful responses from language models.
Prompt engineering involves crafting system prompts, few-shot examples, and output format instructions that guide a model toward desired behavior. Techniques include chain-of-thought reasoning, role assignment, and structured output constraints.
In document intelligence, prompt engineering determines answer quality. A well-designed prompt instructs the model to cite sources, acknowledge uncertainty, and format responses for the specific use case — whether that is legal analysis, financial summarization, or compliance review.
More ai/ml Terms
Retrieval-Augmented Generation (RAG)
An AI architecture that combines information retrieval with text generation to produce answers grounded in source documents.
Vector Embedding
A numerical representation of text as a high-dimensional vector, enabling semantic similarity comparisons between passages.
BM25
A probabilistic keyword-ranking algorithm that scores documents by term frequency and inverse document frequency.
Chunking
The process of splitting large documents into smaller, overlapping segments optimized for retrieval and embedding.
Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information.
Large Language Model (LLM)
A neural network trained on massive text corpora that can understand and generate human language.
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