Learn about how to prompt Command R: Understand the structured prompts used for RAG, formatting chat history and tool outputs, and changing sections of the prompt for different tasks.
A mixture of reflections, literature reviews and an experiment on Automated Prompt Engineering for Large Language Models
This article introduces a practical agent-engineering framework for the development of AI agents, focusing on the key ideas and precepts within the large language model (LLM) context.
An article discussing the concept of monosemanticity in LLMs (Language Learning Models) and how Anthropic is working on making them more controllable and safer through prompt and activation engineering.
Anthropic has introduced a new feature in their Console that allows users to generate production-ready prompt templates using AI. This feature employs prompt engineering techniques such as chain-of-thought reasoning, role setting, and clear variable delineation to create effective and precise prompts. It helps both new and experienced prompt engineers save time and often produces better results than hand-written prompts. The generated prompts are also editable for optimal performance.
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.
