How to score and qualify leads with LLMs

The launch of Refuel-LLM-2 highlighted the need for an efficient lead qualification process due to overwhelming demand and varying lead quality. The company decided against setting up a traditional CRM, opting instead to automate the lead vetting process using AI and LLMs. By leveraging Refuel for this purpose, they were able to upload historical inbound submission data and define tasks and rules for determining lead quality. The system was configured to search the internet for additional lead data, and human feedback was incorporated to refine the process through few-shot prompting. Ultimately, the model was deployed as an endpoint connected with Zapier, allowing the team to receive notifications of qualified leads with confidence scores. This automation reduced time spent on manual lead qualification, ensured consistency and eliminated biases, integrated seamlessly with existing tools like Slack, and allowed dynamic adaptation to business needs while balancing data collection with user friction.

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