#prompt chaining

Rivet is a cutting-edge desktop application that facilitates the development of advanced AI agents and prompt chaining. It integrates seamlessly with technologies such as OpenAI GPT models, Anthropic Claude, and AssemblyAI's LeMUR, providing powerful embedding and vector database capabilities with OpenAI Embeddings and Pinecone. The IDE supports robust Speech-to-Text services, enabling enriched interactions with modern AI frameworks. Comprehensive documentation and flexible TypeScript libraries are available for customized application integration. Engage with a thriving community and actively participate in ongoing development to maintain a leading edge in AI advancements.

The project demonstrates how prompt chaining can enhance fact-checking accuracy with LLMs. It highlights the process of self-interrogation and assumption validation to refine responses. Learn how LLMs verify assumptions and craft reliable conclusions by using the Python script or Jupyter notebook provided. Suitable for users exploring modern fact-checking techniques.

Explore a small library that aims to simplify the process of coding with large language models through streamlined prompt chaining. The library supports the creation of applications such as Retrieval-Augmented QA and Chat with memory, requiring minimal code. Compatible with backends like OpenAI, Hugging Face, and Python, it features prompt visualization and integration with external data sources. MiniChain distinguishes itself with the separation of prompt text using Jinja templates and effortless function annotation, ideal for developers focusing on simplicity without losing functionality.