LLMs
LLM use started like we use search engines with questions/answers. From simple Q/A, we quickly moved to task-oriented use like summarizing text, writing exercises, and text comparison.
LLMs with Retrieval-Augmented Generation (RAG)
LLMs have the world's knowledge, but they don't know about real-time, proprietary, or personal information. RAG is a technique to add custom data to LLMs, providing contextual data for your LLM prompts. RAG enables better responses by keeping information up-to-date and relevant to your specific needs.
LLM Agents
We’ve been able to interact with LLMs and get great responses, but the next step is to automate those responses. LLM Agents are a group of prompts that make an automated workflow to execute an outcome. They're not just chatbots – they're AI entities with agency, capable of taking actions, interacting with other agents, and making decisions.
For example, a lead generation AI agent team could include:
- An inbox management agent that categorizes emails and routes important leads.
- A lead qualification agent that responds to queries and schedules appointments.
- A lead enrichment agent that automatically researches and updates lead information in your CRM.
- A lead nurturing agent that sends personalized follow-ups based on lead data.
This AI agent team can work 24/7, reducing costs and increasing operational efficiency. The future of AI in business isn't just about information retrieval – it's about creating autonomous workers that can handle job functions, allowing us to focus on more high-value tasks.