Pongo is an innovative solution that significantly reduces hallucinations in Language Model (LLM) outputs by 80%. By leveraging their advanced semantic filter technology, Pongo greatly improves the accuracy of RAG (Retrieve, Analyze, Generate) pipelines. This breakthrough technology ensures that incorrect and partially correct answers are minimized, delivering more reliable results.
One of the key advantages of Pongo is its seamless integration into existing RAG pipelines. With just one line of code, companies can incorporate Pongo’s semantic filter to enhance the accuracy of their pipelines. This means that organizations can improve the quality of their outputs without requiring extensive modifications to their existing workflows.
Pongo’s approach is based on combining different types of models and retrieval methods to generate a final score for each document. By leveraging multiple state-of-the-art semantic similarity models and their proprietary ranking algorithm, Pongo ensures that users always receive the most relevant and accurate information.
To ensure optimal performance, Pongo is designed to work with existing pipelines seamlessly. Whether you use a vector database or Elasticsearch, Pongo can be easily integrated into your workflow. By sending your top 100-200 search results to Pongo, you can receive highly relevant and accurate outputs in return.
If you are interested in improving the accuracy of your RAG pipelines and reducing LLM hallucinations, Pongo is the solution for you. Experience the benefits of Pongo’s semantic filter technology and enhance the reliability of your outputs.
Learn more about Pongo by visiting their website: Pongo.