AI - Language & Behavior Prompt Engineering-Training
A prompt engineer is a professional who specializes in crafting and refining prompts for AI systems, particularly those based on natural language processing. Designing and refining prompts—questions or instructions—to elicit specific responses from AI models. Think of it as the interface between human intent and machine output.
For instance, if you've ever interacted with voice assistants like Siri or Alexa, you've engaged in a basic form of prompt engineering. The way you phrase your request—"Play some relaxing music" versus "Play Beethoven's Symphony"—can yield vastly different results.
None of these skills are technical, but Soft Skills based on Behavior & Language
What is Chat-GPT
OpenAI's Generative Pre-trained Transformer is a model, with billions of parameters that showcased an unprecedented ability to generate coherent, contextually relevant, and often indistinguishable from human, text. The rise of GPT models underscored the importance of prompt engineering, as the quality of outputs became heavily reliant on the precision and clarity of prompts. Meaning that the Language you use to feed Chat-GPT relies on the clarity and accuracy of words you use, the order, the sentence structure and the outcome you are looking for.
The importance of language-based AI prompts - Why Language and Behavior?
Every word in a prompt matters. A slight change in phrasing can lead to dramatically different outputs from an AI model. For instance, asking a model to "Describe the Eiffel Tower" versus "Narrate the history of the Eiffel Tower" will yield distinct responses. The former might provide a physical description, while the latter delves into its historical significance.
Understanding these nuances is essential, especially when working with LLMs. These models, trained on vast datasets, can generate a wide range of responses based on the cues they receive. It's not just about asking a question; it's about phrasing it in a way that aligns with your desired outcome.
The 5-step model of training
- Instruction. This is the core directive of the prompt. It tells the model what you want it to do. For example, "Summarize the following text" provides a clear action for the model.
- Context. Context provides additional information that helps the model understand the broader scenario or background. For instance, "Considering the economic downturn, provide investment advice" gives the model a backdrop against which to frame its response.
- Input data. This is the specific information or data you want the model to process. It could be a paragraph, a set of numbers, or even a single word.
- Output indicator. Especially useful in role-playing scenarios, this element guides the model on the format or type of response desired. For instance, "In the style of Shakespeare, rewrite the following sentence" gives the model a stylistic direction.
- Industry language. Language-identified industries, finances, pharma or lawyers don’t use the same language or words to generate “sentiment” on the audience they want to attract. The right “Keywords” embedded in the message make the difference between failure and success. Subject, persona, buyer, client or target are the same, but vary based on the industry you belong to.
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