Prompt or "Meta-Prompt", that's the question 💀🪶

Research & Innovation 🧮

This week, our spotlight is on prompts and the buzz ignited by the recent article titled "PROMPT ENGINEERING: A PROMPT ENGINEER," published on November 9th by Qinyuan Ye and the team. 🚀✨ .

In the midst of 2023, discussions about the imperative need to enhance and technify the interaction between intelligent agents and humans have been fervent. Out of this discourse emerged what some initially dubbed the "career of the future" – prompt engineering.

Essentially, prompt engineering is the structured and organized way, to construct instructions that enhance the precision of AI agent responses. From the essential components of a prompt (persona or roles, context, structured instructions, examples) to the myriad NLP tasks it can facilitate (prompt for transformation, summarization, inference, entity extraction, etc.), the possibilities for refining responses seemed like a hastily launched spacecraft.

Recent articles from highly reputable sources presenting empirical evidence that AI-generated prompts yield more accurate results than manually crafted prompts by humans. As anticipated from the surge in prompt recipes, it swiftly transitioned to the debate of what seemed like an impending demise.

Not to undermine the complexity of prompt creation – it's a well-established fact that common sense is the least common of senses. However, the desired structure for following AI instructions is better understood by AI than by humans at this juncture. As evidenced in articles, humans find themselves in an exploratory and experimental phase within this rapidly evolving landscape of AI.

We invite you to try the conversation with agents who have already read the articles 👇

The repo! 👾

Spilling some prompt tea this time – we've got the inside scoop on the TypeChat repo!🤫. TypeChat simplifies natural language interface development using types. It replaces prompt engineering with schema engineering. Developers define types representing intents, making it easy to categorize sentiment or create complex structures. TypeChat handles prompt construction, validates LLM responses, and ensures alignment with user intent. More dev oriented in contrast to Prompt Flow or Prompt layer.

New at CodeGPT 🎁

You can now integrate CodeGPT with LangChain 👏!

You can create agents in the CodeGPT interface, load them with repositories from GitHub, PDFs, URLs, and more. Plus, you can seamlessly combine these capabilities with all the tools LangChain offers.

You also get the ability to create RAG (Retrieval-Augmented Generation) Agents in CodeGPT and merge them with LangChain's Autonomous Agents. In the provided Python code snippet, learn how to use your API Key and Agent ID to create and run a large language model (LLM) with LangChain.

Want to give it a try? Simply sign up at https://codegpt.co to obtain your API Key and Agent ID. Access the ready-to-run Google Colab notebook here: https://lnkd.in/g5Fm9Kgg! 🚀