Dr. Lance B. Eliot is a world-renowned expert on artificial intelligence with over 8.1+ million amassed views of his AI columns and been featured on CBS 60 Minutes. As a CIO/CTO seasoned executive and high-tech entrepreneur, he combines practical industry experience with deep academic research.
In today’s column, I will explain three new best practices for coping with prompt wording sensitivities when using generative AI and large language models .
This analysis of an innovative proposition is part of my ongoing Forbes.com column coverage on the latest in AI including identifying and explaining various impactful AI complexities . For those of you specifically interested in prompting and prompt engineering, you might want to take a see my comprehensive analysis and description of over fifty notable prompting techniques atI will jump right into my three key recommendations.
The gist is that if the AI you are using is relatively versed in the question or matter you are asking about, the capability of interpreting your prompt tends to go up. The AI has a basis for getting the drift. In contrast, an AI that doesn’t have data training on the given topic will likely flail around to figure out what in the world you are asking about.
A state-of-the-art prompt engineer realizes that if you give an LLM even just one example in a prompt, suggesting what the user intends, this makes a significant difference regarding how well the AI responds to the prompt. Things can readily veer into outer space with multi-shot examples. For example, two major disconcerting problems often occur. I see this lamentedly happen all the time while instructing classes on prompt engineering and guiding attendees to avoid prior adverse practices that they’ve formed as bad habits.
Generative AI Large Language Models LLM Prompt Engineering Techniques Tactics Strategies Openai Chatgpt GPT-4O O1 Google Gemini Anthropic Claude Meta Llama Best Practices
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