OpenAI lately posted a guide to Prompt Engineering. The guide lists 6 techniques for eliciting improved responses from their GPT versions, with a unique concentration on examples for their hottest edition, GPT-4.
The guide’s six substantial-degree tactics are: create crystal clear directions, offer reference textual content, split sophisticated jobs into simpler subtasks, give the model time to “imagine”, use external tools, and test modifications systematically. Each and every of the tactics is broken down into a set of unique, actionable tactics with case in point prompts. Numerous of the practices are centered on results of LLM analysis, these kinds of as chain-of-imagined prompting or recursive summarization.
OpenAI’s study paper on GPT-3, revealed in 2020, confirmed how the model could complete a variety of normal language processing (NLP) tasks employing several shot learning basically, by prompting the design with a description or examples of the activity to
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