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JigsawStack Mixture-Of-Agents (MoA): Outperform any single LLM and reduce cost with Prompt Engine - JigsawStack

Dec 06, 2024 - jigsawstack.com
The article discusses the use of JigsawStack's Mixture-of-Agents (MoA) and Prompt Engine to improve the performance of Language Learning Models (LLMs). The MoA approach combines multiple LLMs to provide better consistency and quality in applications, while the Prompt Engine simplifies the process of moving between models and controlling costs. The Prompt Engine works by creating a prompt, enhancing it for accuracy, and selecting the best LLMs for the task. The engine then executes the prompt, ranks the output, and combines the best results into a single output.

The article also highlights the benefits of using the Prompt Engine, including increased quality and consistency, reduced hallucinations, and cost savings through prompt caching. The engine also learns over time, improving the speed and quality of outputs. The Prompt Engine models are regularly updated with the latest models from providers like OpenAI, Anthropic, MistralAI, and Hugging Face, ensuring backward compatibility. The article concludes by inviting developers to join the JigsawStack community on Discord and Twitter.

Key takeaways:

  • JigsawStack's Mixture-of-Agents (MoA) approach allows applications to use more than one language learning model (LLM), improving consistency and quality of output.
  • Prompt Engine is a tool developed by JigsawStack to simplify the process of using multiple LLMs, it enhances the initial prompt, reduces token usage, and prevents output structure breakage.
  • The Prompt Engine uses a bucket of 5 best LLMs related to the prompt, runs them in parallel, and combines the top 2 to 3 outputs into a single output, improving quality and consistency while reducing hallucinations.
  • JigsawStack ensures backward compatibility when upgrading to newer models, preventing code breakage, and also provides a community for developers to share projects, ask questions, and collaborate.
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