Prompt Engineering

Prompt Engineering explores effective communication with AI, focusing on techniques like prompt crafting and 'chain of thought' prompting. It covers advancements in AI models like LLaMA, Orca, and ChatGPT, discusses anthropomorphizing AI, and addresses concepts like the black box problem and AI's societal impact.

AI Communication Techniques AI Model Advancements Prompt Crafting Strategies Societal Impact of AI AI in Image Generation AI Role Assignment Speech Recognition

The hottest Substack posts of Prompt Engineering

And their main takeaways
196 implied HN points • 05 May 23
  1. The biggest deal in AI is the open-source model LLaMA, not ChatGPT.
  2. ChatGPT was impressive but had weaknesses like generating nonsense and being easily fooled.
  3. The rapid innovation cycle after the leak of LLaMA weights led to significant advancements in AI models.
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39 implied HN points • 22 May 23
  1. AI is rapidly advancing, especially in the medical field.
  2. New technology like ImageBind can link different types of data with images as a common basis.
  3. Fine-tuning language models with a small number of prompts can significantly improve performance.
19 implied HN points • 30 May 23
  1. Large language models perform better when given a specific role in conversations
  2. Assigning roles to language models can lead to more relevant and engaging responses
  3. Providing clarity on the intended role of a language model is a powerful way to enhance its performance
19 implied HN points • 28 May 23
  1. ChatGPT conversations are now shareable to prevent screenshot sharing and misinformation.
  2. Tree-of-thoughts prompting is a new approach where LLM is prompted with multiple initial steps and evaluates each one.
  3. A new highly performant open-source model called Guanaco outperforms previous models and was fine-tuned using a new approach named QLoRA.
1 HN point • 11 May 23
  1. AI advancements are fast and significant, leading to uncertainties about the future models of society.
  2. AI may result in mass unemployment, but historically, technology revolutions have not led to catastrophic unemployment levels.
  3. Challenges with AI include the misuse of data leading to bias, the potential for AI to outsmart humans, and the widening class divide due to unequal access to AI tools.
0 implied HN points • 14 Jun 23
  1. OpenAI has updated the GPT-3.5 model with a larger context length of 16,384 tokens, allowing for more data input and better results.
  2. There is a 75% cost reduction on OpenAI's models, and a 25% cost reduction on input tokens for gpt-3.5-turbo.
  3. OpenAI announced functions that allow the model to make API calls, write emails, query databases, and more, taking LLM applications to the next level.
0 implied HN points • 05 Jul 23
  1. OpenAI's Whisper model is a powerful tool for audio to text transcription, trained on 680,000 hours of data.
  2. Voice interfaces are often tied to specific software, but a general-purpose voice transcriber like Whisper could be very useful.
  3. Whisper can be integrated with tools like ChatGPT for recording and transcribing text to work on creating a stronger narrative.
0 implied HN points • 28 Apr 23
  1. New content is coming soon on
  2. The post includes an image and a link to Marcel Salathé's profile.
  3. The post encourages sharing and subscribing for updates.