This article discusses the art and science of prompt engineering for large language models (LLMs), providing an overview of basic and advanced techniques, recent research, and practical strategies for improving performance.
Langfuse is an open-source LLM engineering platform that offers tracing, prompt management, evaluation, datasets, metrics, and playground for debugging and improving LLM applications. It is backed by several renowned companies and has won multiple awards. Langfuse is built with security in mind, with SOC 2 Type II and ISO 27001 certifications and GDPR compliance.
This tutorial introduces promptrefiner, a tool created by Amirarsalan Rajabi that uses the GPT-4 model to create perfect system prompts for local LLMs.
An in-depth guide about Mistral 7B, a 7-billion-parameter language model released by Mistral AI. This guide includes an introduction to the model, its capabilities, code generation, limitations, guardrails, and enforcing guardrails. It also covers applications, papers, and additional reading materials related to Mistral 7B and finetuned models.
This article explores the application of XML Schema in AI systems and prompts. XML Schema provides a structured way to describe and validate data, making it an essential tool for AI systems that deal with data. The author discusses how XML Schema can be used to create and manage data in AI applications, such as speech recognition and natural language processing. The article also covers the benefits of using XML Schema in AI systems, including improved data consistency, interoperability, and security. Lastly, the author provides some examples of XML Schema usage in AI systems and discusses the future of XML Schema in AI technology.
Open-source tools for prompt testing and experimentation, with support for both LLMs (e.g. OpenAI, LLaMA) and vector databases (e.g. Chroma, Weaviate, LanceDB).
Tips on improving your GitHub repository organization and structure. Bullet Points:
- Create meaningful branch names - Use descriptive commit messages - Keep a clean project history
- Separate your code into well-organized directories - Follow a consistent naming convention - Make use of pull requests
- Collaborate effectively by writing clear documentation - Maintain good communication within your team Keywords: GitHub, repository best practices, organization, structure, branch names, commit messages, project history, directories, naming conventions, pull requests, collaboration, documentation, effective communication
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