RAG is an AI architecture that combines retrieval (pulling relevant information from a knowledge base) with generation (using an LLM to produce output). Instead of relying solely on what an LLM learned during training, RAG gives the model access to your specific documents, CRM data, and company knowledge in real time. For marketing, RAG enables AI tools that can generate on-brand, factually accurate content using your actual product docs, case studies, and customer data.
For example, a RAG-powered AI content tool might pull from your CRM to find relevant case studies, your product docs for accurate feature descriptions, and your brand guidelines — generating copy that actually sounds like your company, not a generic SaaS startup.
We build RAG pipelines for clients who need AI content that's accurate and on-brand — not generic output that embarrasses the marketing team.
Relevant Cactus Services
We implement Retrieval-Augmented Generation (RAG) strategies for B2B tech startups every day. Book a free 30-minute call to get a concrete plan for your situation.
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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.
Large Language Model (LLM)
An LLM is the AI model underlying most modern AI tools — GPT-4, Claude, Gemini, Llama.
Prompt Engineering
Prompt engineering is the practice of designing inputs to AI models to get better, more consistent outputs.