Prompt Engineering
The practice of designing inputs to LLMs to elicit accurate, consistent, useful outputs.
Prompt engineering is the practice of designing inputs to large language models to elicit accurate, consistent, and useful outputs. Modern prompt engineering combines techniques like few-shot examples, chain-of-thought reasoning, structured output (JSON mode, function calling), system prompts, role assignment, and prompt chaining across multiple LLM calls. Production prompt systems require version control, evaluation frameworks (e.g. Promptfoo, Inspect, custom eval harnesses), regression testing, and cost monitoring. Empire325 designs production prompt systems with rigorous evaluation, automated regression detection, and clear separation between business logic and prompt content.
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Large Language Model (LLM)
A neural network trained on massive text corpora to understand and generate human language.
Retrieval-Augmented Generation (RAG)
An AI architecture combining LLM generation with real-time retrieval from external knowledge sources.
AI Agent
An autonomous LLM-based system that plans, takes actions via tools, and accomplishes multi-step goals.
Fine-Tuning
Adapting a pretrained foundation model to specific tasks or domains via additional training.