Explore the innovative techniques and strategies we've implemented to enhance the accuracy of embedding-based retrieval systems.

Discover how our team's journey from building mission-critical systems led to the creation of Parlant, an open-source Conversational AI engine designed for reliable customer interactions.

In 2025, the most effective way to build customer-facing AI agents combines the adaptability of LLMs with the structure and precision of traditional conversation design.

The challenges of LLM hallucinations in large-scale conversational AI and strategies for mitigating them.

Conversational AI has evolved dramatically over decades. From rule-based chatbots like ELIZA in the 1960s, through hybrid ML+rule frameworks, and, recently, LLMs. Now, state-of-the-art Conversation Modeling engines marry the generative power of LLMs with structured guidelines, allowing richer interactions that remain controllable, easier to develop iteratively, and more scalable to test in real-world scenarios.

In the market for generic RAG frameworks, the different providers are fighting over who can provide 67% accuracy versus 65%. And when you run an off-the-shelf RAG framework on your use case, it will end up closer to 50% accuracy. Is this the best that the industry can do?

A commentary upon Yann LeCun's key note at AI Action Summit, along with some supplementary explanations on how LLMs work under the hood