An LLM is the AI model underlying most modern AI tools — GPT-4, Claude, Gemini, Llama. They're trained on massive text datasets and can understand and generate human language, code, and structured data. In marketing, LLMs power copywriting tools, research summarization, personalization at scale, and the AI agents that run autonomous workflows. Understanding which LLM a tool uses and its tradeoffs (speed, cost, accuracy, context length) matters for production workflows.
For example, GPT-4 is better at complex reasoning tasks like analyzing a prospect's annual report, while Claude is known for long-context handling — useful for processing long documents to extract personalization signals.
We use multiple LLMs in our workflows depending on the task — routing different steps to the model best suited for accuracy, speed, or cost.
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Agentic AI refers to AI systems that can plan, take actions, use tools, and complete multi-step tasks autonomously — going beyond generating text to actually doing work.
AI Agent
An AI agent is an LLM-powered system that can autonomously use tools, access data, and complete tasks — as opposed to a simple chatbot that only responds to single prompts.
Autonomous Workflow
An autonomous workflow is a multi-step automated process that runs without human intervention — trigger, conditions, actions, branches, and loops all executing on schedule or in response to events.
Human-in-the-Loop (HITL)
Human-in-the-loop describes AI automation workflows that include a human review or approval step before consequential actions are taken — particularly sending outreach, making calls, or publishing content.
Prompt Engineering
Prompt engineering is the practice of designing inputs to AI models to get better, more consistent outputs.
Retrieval-Augmented Generation (RAG)
RAG is an AI architecture that combines retrieval (pulling relevant information from a knowledge base) with generation (using an LLM to produce output).