The article discusses guiding a Language Model (LLM) in creating structured output. LLMs are powerful tools in natural language processing, but they often generate output that is unstructured and lacks coherence. The author suggests using a technique called “prompt engineering” to guide the LLM towards producing more structured and coherent responses. By providing specific prompts and examples, the LLM can be trained to generate output that is more aligned with the desired structure. This approach can be particularly useful in tasks such as summarization, question answering, and dialogue generation. The article emphasizes the importance of carefully designing prompts and providing feedback to the LLM to improve its performance. Ultimately, by guiding the LLM’s response, researchers and developers can harness the full potential of these powerful language models in a variety of applications.
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Source link: https://towardsdatascience.com/guiding-an-llms-response-to-create-structured-output-5dde0d3e426b
Guiding LLMs to Create Structured Output for Data Science #StructuredOutput
