The hottest Prompt engineering Substack posts right now

And their main takeaways
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Advanced Prompt Engineering

Deep (Learning) Focus • 609 implied HN points • 08 May 23
🕹 Technology AI/ML Deep Learning Prompt engineering Information Retrieval
  1. LLMs can solve complex problems by breaking them into smaller parts or steps using CoT prompting.
  2. Automatic prompt engineering techniques, like gradient-based search, provide a way to optimize language model prompts based on data.
  3. Simple techniques like self-consistency and generated knowledge can be powerful for improving LLM performance in reasoning tasks.

Practical Prompt Engineering (Part One)

Deep (Learning) Focus • 373 implied HN points • 01 May 23
🕹 Technology AI Machine Learning Prompt engineering Chatbots Deep Learning
  1. LLMs are powerful due to their generic text-to-text format for solving a variety of tasks.
  2. Prompt engineering is crucial for maximizing LLM performance by crafting detailed and specific prompts.
  3. Techniques like zero and few-shot learning, as well as instruction prompting, can optimize LLM performance for different tasks.

E3 - 🛠️ Prompt Engineering Basics and 50+ Free 🎁 Product Management Prompts 🚀

The Product Channel By Sid Saladi • 20 implied HN points • 24 Nov 24
🕹 Technology AI Tools Product Management Software Development Prompt engineering Data Analysis
  1. Prompt engineering is about crafting the right questions to get useful responses from AI. Think of it like asking the AI to help you with specific tasks in a clear way.
  2. This skill can help product managers speed up their work by automating tasks and generating creative ideas. It's a powerful tool for making better decisions based on data.
  3. Understanding how to structure prompts effectively can lead to more relevant and accurate results. It involves giving clear instructions, context, and examples to guide the AI.
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An Introduction To DSPy

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 28 May 24
🕹 Technology Programming Machine Learning Software Development Artificial Intelligence Prompt engineering
  1. DSPy is a programming tool that simplifies how we work with language models by separating the tasks from the prompts. This means you tell DSPy what to do, not how to do it.
  2. It uses something called 'signatures' to describe tasks in a simple way, which helps in generating and optimizing prompts automatically. This reduces the need for manual prompt crafting.
  3. DSPy offers an iterative workflow for optimizing language tasks, making it suitable for complex applications. It can improve performance with minimal effort by tweaking how it uses language models.

Concise Chain-of-Thought (CCoT) Prompting

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 59 implied HN points • 24 Jan 24
🕹 Technology AI Machine Learning Prompt engineering Natural Language Data Analysis
  1. Concise Chain-of-Thought (CCoT) prompting helps make AI responses shorter and faster. This means you save on costs and get quicker answers.
  2. Using CCoT, the response length can be reduced by almost 50%, but it can lead to lower performance in math problems. So, it’s a trade-off between speed and accuracy.
  3. For cost-saving in AI, focusing on reducing the number of output tokens is key since they are generally more expensive. CCoT is one way to achieve this without sacrificing performance too much.

Week 75 - E3 - 🛠️ Prompt Engineering Basics and 50+ Free 🎁 Product Management Prompts 🚀

The Product Channel By Sid Saladi • 23 implied HN points • 21 Jan 24
🕹 Technology AI Product Management Prompt engineering Generative AI
  1. Prompt engineering is crafting effective natural language prompts to get desired outputs from AI.
  2. Prompt engineering is crucial for product managers to unlock AI potential in workflows and decision-making.
  3. Well-structured prompts include clear instructions, context, format, and tone, enhancing coherency and relevance.

Data Delivery To Large Language Models [Updated]

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 06 Dec 23
🕹 Technology Artificial Intelligence Machine Learning Data science Natural Language Processing Prompt engineering
  1. Every effective AI strategy needs a solid data strategy that includes data discovery, design, development, and delivery.
  2. At inference, providing the right context and relevant data is crucial to help language models produce accurate responses.
  3. Training models involves two key phases: meta-training for foundational knowledge and meta-learning for fine-tuning on specific tasks.

The Chain-Of-X Phenomenon In LLM Prompting

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 20 Nov 23
🕹 Technology AI NLP Machine Learning Human-computer interaction Prompt engineering
  1. Chain-of-thought prompting helps large language models break down complex problems. This makes it easier for them to solve tasks step by step, just like humans do.
  2. Using chain-of-thought techniques improves the transparency of LLMs. It allows users to see how the model arrives at its answers, which can reduce mistakes.
  3. Different prompting methods, like least-to-most prompting, can be combined with chain-of-thought techniques. This flexibility can enhance the performance of models in various tasks.

Are Emergent Abilities In LLMs Inherent Or Merely In-Context Learning?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 16 Nov 23
🕹 Technology AI Machine Learning Natural Language Data science Prompt engineering
  1. Emergent abilities in language models (LLMs) allow them to perform well on tasks they weren't specifically trained for. This shows a level of flexibility in handling diverse challenges.
  2. These abilities might not be hidden skills but rather show how LLMs learn through in-context examples. This means that understanding context plays a big role in their performance.
  3. As LLMs get larger and better, we see improvements in their skills, often influenced by new ways of giving them instructions, indicating that these skills can expand with better training techniques.

A New Prompt Technique From DeepMind Called Optimisation by PROmpting (OPRO)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 02 Nov 23
🕹 Technology AI Machine Learning NLP Prompt engineering Data science
  1. A new technique called Optimisation by PROmpting (OPRO) helps improve the performance of language models by using specific prompts. This method aims to make prompts more effective without changing the underlying models.
  2. OPRO can generate multiple prompt options at once, allowing the system to find the best one more efficiently. This strategy is helpful for solving tasks and provides better stability in results.
  3. The prompts created with OPRO can perform 8% to 50% better than those designed by humans, showing it can be more efficient in certain tasks. It's a new way to help machines understand and respond more accurately.

A New Prompt Engineering Technique Has Been Introduced Called Step-Back Prompting

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 12 Oct 23
🕹 Technology AI NLP Data science Prompt engineering Machine Learning
  1. Step-Back Prompting helps Large Language Models find better answers by simplifying complex questions. It turns a detailed question into a more generic one that's easier to tackle.
  2. This technique can be combined with other methods to improve accuracy and effectiveness. It shows promise in fixing errors from traditional approaches.
  3. Using Step-Back Prompting requires careful thought and might work best with autonomous systems. It's a more advanced method compared to static prompting.

Chain-Of-Thought Prompting In LLMs

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 20 Apr 23
🕹 Technology AI Machine Learning NLP Chatbots Prompt engineering
  1. Chain-of-thought prompting helps large language models break down complex tasks into smaller, manageable steps. This makes it easier for them to solve problems.
  2. Using chain-of-thought reasoning in prompts can improve how well language models perform on tasks by allowing them to show their reasoning process.
  3. This method is especially useful for tasks that require common sense or math, making it similar to how humans approach problem-solving.

The Evolution Of Prompt Engineering

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 17 Apr 23
🕹 Technology AI NLP Generative AI Chatbots Prompt engineering
  1. Prompt engineering is important for getting the best responses from large language models. Users have to carefully design prompts to mimic what they want the model to generate.
  2. Static prompts can be turned into templates with placeholders that can be filled in later. This makes it easier to reuse and share prompts in different situations.
  3. Prompt pipelines allow users to create more complex applications by linking several prompts together. This helps organize how information is processed and improves user interaction with chatbots.

Chain-of-Symbol Prompting (CoS) For Large Language Models

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 29 Jan 24
🕹 Technology Artificial Intelligence Natural Language Processing Machine Learning Prompt engineering
  1. LLMs struggle with understanding complex spatial tasks using just natural language. This research focuses on improving their ability to navigate virtual environments.
  2. The new Chain-of-Symbol Prompting (CoS) method helps LLMs represent spatial relationships more effectively. It leads to much better performance in planning tasks compared to traditional methods.
  3. Using symbols instead of natural language makes it easier for LLMs to learn and reduces the number of tokens needed in prompts. This results in clearer and more concise representations.