Support teams across functions are a long-standing advocate of chatbots. With time, more layers of technology like decision trees, NLP, unsupervised learning, etc., were added to enrich the interactions. However, they failed to deliver on the original promise of relevant responses, reduced support costs, and effortless scalability.

Now, Large language models (LLMs) are transforming the entire support landscape, including customer, IT, and HR support teams. Their capability to hold contextual and personalized interactions at scale has placed LLMs on center stage.

However, LLMs, too, need to overcome challenges like hallucination, inaccuracy, and out-of-date data. That’s where more cutting-edge frameworks help overcome the hurdles.

Watch Alan Pelz Sharpe, Founder & Principal Analyst, Deep Analysis, and Vishal Sharma, CTO, SearchUnify, as they discussed the evolution of chatbots and what next-gen chatbots need to deliver contextual and intent-driven responses for unparalleled customer experiences.

Key discussion points include:


How LLM-powered chatbots recognize the intent, semantics, and context in support queries


How Federated Retrieval Augmented Generation (FRAG) framework makes support interactions relevant and accurate


How LLM-powered bots help cut costs and automate resolution delivery

Who’s it for?


Customer support leaders, IT support specialists, HR support managers, and In-product support specialists