This content originally appeared on DEV Community and was authored by Adipta Martulandi
Large Language Models (LLMs) like GPT are powerful tools for generating human-like text, but they are not without their challenges. One common issue is hallucination, where the model produces incorrect, misleading, or nonsense outputs. Understanding why hallucinations occur and how to address them is critical to effectively using these models.
Hallucinations in LLMs refer to instances where the model generates text that is factually inaccurate or contextually irrelevant.
Prompt engineering is the art and science of designing effective prompts to interact with large language models (LLMs) like GPT. At its core, it involves crafting instructions or queries in a way that guides these models to produce desired, accurate, and meaningful outputs. As LLMs grow increasingly sophisticated, prompt engineering has emerged as a critical skill for unlocking their full potential and reducing hallucinations.
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This content originally appeared on DEV Community and was authored by Adipta Martulandi
Adipta Martulandi | Sciencx (2025-01-17T02:04:06+00:00) Prompt Engineering Techniques to Improve LLMs Performance. Retrieved from https://www.scien.cx/2025/01/17/prompt-engineering-techniques-to-improve-llms-performance/
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