I have been talking with three AIs about conversation etiquette with a LLM. Here is what they said…
Effective Communication with Large Language Models: Best Practices and Principles
In the rapidly evolving landscape of artificial intelligence, engaging with language models such as large language models (LLMs) requires strategic communication techniques. Based on insights gathered from discussions with multiple AI systems, this article outlines essential principles to enhance the quality, efficiency, and clarity of your interactions with these powerful tools.
- Prioritize Clarity and Specificity
Why It Matters:
Vague prompts often yield equally vague responses. LLMs operate by identifying patterns within the data they were trained on. The more precisely you define your query, the better the model can return relevant and accurate information.
Recommended Approach:
Avoid broad or ambiguous instructions. Instead, specify the scope, context, and desired format of your request.
Example:
– Less Effective: “Tell me about the economy.”
– More Effective: “Summarize the main arguments for and against Modern Monetary Theory (MMT), aimed at college-educated readers. Present the information as a clear, bulleted list.”
- Define Role and Context
Why It Matters:
Framing the AI’s role within a conversation helps guide the tone, depth, and focus of responses, aligning them with your expectations.
Recommended Approach:
Set the AI’s persona or perspective at the beginning of your prompt to tailor the output accordingly.
Example:
– Less Effective: “Explain quantum computing.”
– More Effective: “Act as a university professor giving a first-year lecture on quantum computing. Use analogies where appropriate and define key terms like ‘superposition’ and ‘entanglement.'”
- Embrace Iterative Refinement
Why It Matters:
Initial outputs may not always fully meet your needs. Engaging in a back-and-forth allows for corrections, clarifications, and honing of content, fostering a collaborative process.
Workflow Example:
– You: “Draft an email to my team about the project deadline being moved forward.”
– AI: Provides a draft.
– You: “Make the tone more urgent and add a request for a risk assessment by Friday.”
– AI: Provides a revised version.
This iterative process enhances the final output’s relevance and effectiveness.
- Deconstruct Complex Tasks
Why It Matters:
Breaking down complicated requests into manageable steps grants greater control over the results and improves task execution.
Recommended Approach:
Start with a high-level outline, then proceed to detailed prompts for each part.
Example:
– Phase 1: “Outline the key periods in Paris
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