Today, Contextual AI announces the availability of advanced parameters in agent creation and editing.

With these parameters, you can control more granular aspects of your specialized RAG agent across retrieval, reranking, filtering and generation. In particular, you can: 

• Fine-tune retrieval relevance by adjusting lexical and semantic search weightings, helping you balance keyword precision with conceptual matching
• Optimize chunk selection by configuring both the number of retrieved chunks and reranked chunks, allowing you to maximize relevance while managing context window usage
• Customize filtering criteria with your own custom filter prompt, enabling you to implement domain-specific relevance rules
• Control response generation with precise temperature, top_p, and frequency penalty settings, giving you the flexibility to balance consistency and creativity in answers

These controls empower you to optimize your agent according to use case-specific requirements with much greater precision, ultimately delivering more accurate and relevant responses to your users’ queries.

To get started, use the agent_configs object in the Create/Edit Agent APIs. You can also change these parameters by editing the agent in the UI. They are subject to change.