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).
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- Prompt engineering is about experimenting with changes in prompts to understand their impacts on what large language models (LLMs) generate as the output. Prompt engineering yields better outcomes for LLM use with a few basic techniques
- Zero-shot prompting is when an LLM is given a task, via prompt, for which the model has not previously seen data
- For the language tasks in the literature, performance improves with a few examples, this is known as few-shot prompting
- Chain-of-Thought (CoT) prompting breaks down multi-step problems into intermediate steps allowing LLMs to tackle complex reasoning that can't be solved with zero-shot or few-shot prompting
- Built upon CoT, self-consistency prompting is an advanced prompting technique, that provides the LLM with multiple, diverse reasoning paths and then selects the most consistent answer among the generated responses
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