This Prompt Engineering Digest explores AI advancements, including the importance of well-constructed prompts for improved language model performance, a tutorial on LangChain for extracting information from PDFs, AI-generated art through stable diffusion, a comprehensive course on Large Language Models (LLMs), and innovative web browser extensions for enhancing ChatGPT.
Formatting plays a crucial role in prompt engineering. A standard prompt can take the form of a question or an instruction. There are different prompting approaches available. Zero-shot prompting involves directly prompting the model without any prior examples or demonstrations, while few-shot prompting entails providing exemplars or demonstrations to enhance the model’s understanding of the task at hand.
This podcast showcases a video tutorial aimed at individuals seeking to harness the power of large language models for their data analysis endeavors. The tutorial introduces LangChain, a tool specifically designed to extract valuable information from PDF files using OpenAI Text Embeddings.
Users can delve deeper into their PDF documents, extract relevant information, and gain a competitive edge in their data-driven pursuits.
Unlike traditional art, stable diffusion relies on algorithms to generate unpredictable and visually stunning results. The article provides a list of 15 prompts to inspire the creation of AI-generated artwork, ranging from abstract designs and landscapes to portraits and surreal scenes.
The author concludes by encouraging readers to begin their AI art journey.
Led by instructors Isa Fulford from OpenAI and Andrew Ng from DeepLearning.AI, the course covers fundamental concepts of LLMs, provides best practices, and demonstrates the usage of LLM APIs for various tasks, including summarization, inference, text transformation, and expansion.
The course is designed to cater to a wide range of participants, from beginners with a basic understanding of Python to advanced machine learning engineers interested in exploring the forefront of prompt engineering and utilizing LLMs effectively.
While ChatGPT excels in generating detailed and human-like responses, it may not always guarantee accuracy when discussing specific people, places, or facts. However, the extensions outlined here offer additional features to optimize and tailor the chatbot’s capabilities.
The extensions represent the ongoing evolution of AI chatbots and their increasing usability.
While using such prompt engineering techniques and building LLM based applications, don’t forget about security aspects. These two videos might be a good introduction to attacks on LLM.
The easy access to powerful APIs like GPT-4 raises questions about the future of IT security. As large language models (LLMs) are relatively new, the security landscape is expected to evolve rapidly. To stay ahead, it’s important to explore the implications of LLM security. Check out the provided resources for further insights.
In this video, you can delve into quick tricks to influence AI responses, even if the system instructions suggest otherwise. Thus, we get an idea of the limitations of LLM. Check out the resources provided, including the AI series and the game, for further learning.
Stay informed to harness AI’s advantages and be ahead in your domain!
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