Notebook
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My latest post discusses optimizing Marketing spend using reinforcement learning (RL) and an LLM as a meta-controller—an approach we term Agentic-AI. It details creating a custom RL environment for managing multiple Marketing channels and describes how the LLM meta-controller orchestrates the overall learning process. Rather than making spend decisions directly, the LLM guides iterative training,…
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My latest post explores using a large language model (LLM) to directly control a reinforcement learning (RL) environment for optimizing Marketing spend. The LLM actively makes decisions step by step. Unlike typical RL applications, this setup allows real-time interaction and showcases the LLM’s reasoning process while determining optimal actions.
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Check out my latest blog post, where I set up a simple scenario to show how a reinforcement learning (RL) agent finds the sweet spot in marketing spend—laying the groundwork for Agentic AI. Whether you’re curious about RL fundamentals or integrating LLMs, this mini use case offers valuable insight into the inner workings of RL…
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Large Language Models (LLMs) are powerful, but they don’t always surface the most relevant or up-to-date information for your needs. Retrieval-Augmented Generation (RAG) changes that by integrating real-time, domain-specific insights, ensuring AI-generated responses are always up-to-date and relevant. Instead of relying on generic training data, RAG personalizes AI with custom knowledge sources, from reports to…
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In a previous post, we explored the pivotal roles of incrementality and lift in Marketing Decision Sciences. Today, we dive deeper into these measurements using a straightforward example—an A/B split test—to demonstrate how incremental gain and lift are calculated. This discussion also serves as a gentle introduction to some foundational statistical concepts: Data as a…