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Prompting

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Posts tagged with Prompting

Conversational vs Structured Prompting

Learn when to apply conversational versus structured prompting techniques to optimize interactions with large language AI models. Discover how to blend approaches, maximizing creative explorations and personalized results.

Conversational vs Structured Prompting

The emergence of advanced large language models (LLMs) like ChatGPT, Claude, and GPT-4 in 2023 has unlocked new potentials for artificial intelligence. These systems demonstrate an unprecedented ability to understand natural language prompts and generate coherent, human-like responses. However, effectively "prompting" these AI systems to get useful results requires some specialized knowledge and technique. Neglecting prompt crafting can lead to inconsistent or nonsensical output.

As LLM capabilities advance rapidly, two primary approaches to prompting have emerged: conversational and structured. While conversational prompting involves interactively querying the system using plain language, structured prompting requires more precisely encoding instructions to

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Ask Me Anything (AMA) Prompting

Ask Me Anything (AMA) Prompting is a novel strategy that aggregates responses from multiple prompts to enhance conversational AI. This simple approach significantly boosts model accuracy without additional training.

Ask Me Anything (AMA) Prompting

Ask Me Anything Prompting (AMA) is a novel strategy for enhancing the capabilities of large language models (LLMs). This approach, which methodologically collects multiple prompts and aggregates their responses, addresses the brittleness of single-prompt strategies and moves beyond the need for meticulously crafted prompts. It has proven to significantly improve task performance across various model types and sizes, enabling smaller, open-source LLMs to reach or surpass the performance levels of larger models like GPT-4.

Ask Me Anything: A simple strategy for prompting language models
Large language models (LLMs) transfer well to new tasks out-of-the-box
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Plan-and-Solve Plus (PS+) - A Prompting Framework for Enhanced LLM Reasoning

Plan-and-Solve Plus (PS+): A novel prompting framework for enhanced LLM reasoning. Discover powerful techniques like detailed instructions, self-consistency evaluation, and error analysis to empower your models in zero-shot learning.

Plan-and-Solve Plus (PS+) - A Prompting Framework for Enhanced LLM Reasoning

Let's take a look at the Plan-and-Solve paper, something I've been meaning to explore in-depth. Sure, its concepts have been rolled into our CRISP prompting framework, but there's more to unpack here.

The CRISP Prompt Engineering Method: A Dynamic Framework for Advanced AI Reasoning and Decision-Making
AI knowledge without logic is a recipe for bad decisions. CRISP is the missing methodology your LLM needs.

In this article, we're going to break down what the framework is all about and why it's cool. The academic

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Introduction to the AI Prompt Development Process

A 15-step methodology for crafting optimized AI prompts that tap into the full potential of AI systems. The process aims to maximize relevance, consistency and quality of outputs.

Introduction to the AI Prompt Development Process

As artificial intelligence and machine learning continue to evolve rapidly, ongoing enhancement is crucial. This introduces a carefully designed process for developing AI prompts. It is made to be adaptable at its core. Since AI is dynamic, this process is not fixed but a living methodology. It is subject to constant refinement and improvement to meet arising needs and difficulties.

Having a structured process is especially important when collaborating in a team or company where consistency, quality, and cooperation matter. A well-defined process fosters shared understanding, streamlines efforts, and encourages a unified approach among team members. It acts as

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AI's Dicey Reputation: Are LLMs Really Just Random Stochastic Machines?

The dice don't lie - but they also don't tell the whole story of AI. Peel back the layers of the dice analogy to understand how prompts shape possibilities within AI's parameters.

AI's Dicey Reputation: Are LLMs Really Just Random Stochastic Machines?

Not too long ago, I found myself in a spirited exchange on social media with a chap – genuinely a good fellow – who had some reservations about the concepts of prompt engineering and prompting.

His stance revolved around two main points.

  1. Firstly, he believed that prompting and prompt engineering were one and the same, even though we've delved deep into their distinctions in numerous articles.
  2. But the crux of his argument was his likening of prompting to mere "guessing" or "or gambling" drawing a parallel to the simple act of rolling a dice.

This is an issue that I often

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