Reposting AdvisorX AI webinar here: --- For all wealth advisors looking to enhance their productivity and client engagement through AI technology. Here are a few compelling reasons why you should watch this webinar replay: Create Effective Personas: Learn how to craft personas for almost all your services, allowing you to save at least 10+ hours per week on routine and client engagement tasks. Realistic AI Insights: We will demystify the hype around AI and provide practical applications tailored for wealth advisors. Leverage Your Expertise: Discover how to securely inject your domain expertise into large language models (LLMs) to augment your years of hard work and knowledge in creating content for clients and prospects. Advanced Prompting Techniques: Gain insights into the best prompting techniques to get the most out of LLMs for wealth advisors. Core Building Blocks: Understand the essential building blocks of creating effective AI personas that align with your firm's unique needs. AI writing assistants are not just about generating long-form content or complex reports. They can also excel in writing short-form content, extracting summaries from large documents, and more. Imagine having an economic analyst, a virtual CMO, an expert content marketer, a financial analyst, and an estate planning assistant, all fine-tuned for your firm. This session will go beyond custom GPTs. Our AI personas are built on an advisor’s knowledge base, documents, and preferred writing style, giving your AI a unique personality with advanced prompting techniques.

Check out https://voicenotes.com/app It's a dictation app you can use for notes, capturing thoughts, content ideas, reminders, etc. But it also has AI baked right in, and you can ask Voicenotes questions about all the notes you've created over time. Clearly, it becomes more helpful / valuable the more you use it. You can try for free and they're currently offering a $50 single payment lifetime license. I've been playing with it and it's pretty cool and super intuitive and easy to use

I've heard several founders mention that they're using RAG over another way, and wanted to look up what that is (feel free to chime in with more simplification / clarity!) --- **RAG** stands for **Retrieval-Augmented Generation**. It's an advanced approach in natural language processing that combines the strengths of retrieval-based methods and generative models. Here's a detailed explanation of RAG and how it compares to other methods: ### What is RAG? 1. **Retrieval Component**: - **Retrieval-based methods** work by selecting relevant documents or pieces of text from a pre-existing database or corpus. These methods are efficient in fetching factual information directly from the source without generating new content. - In RAG, the retrieval component searches through a large database to find relevant documents or passages related to the query. 2. **Augmented Generation Component**: - **Generative models** like GPT-4 create new text based on patterns learned from the training data. These models are capable of producing coherent and contextually relevant text even when the exact answer isn't present in the training data. - In RAG, the generative component uses the retrieved documents to generate a more accurate and contextually relevant response. It augments the generative model with the retrieved information, enhancing its factual accuracy and relevance. ### How RAG Works 1. **Query Input**: A query is input into the system. 2. **Retrieval Phase**: The system retrieves relevant documents or passages from a large corpus. 3. **Generation Phase**: Using the retrieved documents, the generative model creates a response that is both coherent and factually accurate. ### Comparison to Other Methods 1. **Pure Generative Models**: - **Advantages**: Can generate creative and contextually rich responses. Useful for open-ended questions and generating new ideas. - **Disadvantages**: May hallucinate or produce incorrect information, especially if the required information wasn't in the training data.

by Jeff Morgan, see original article here. Want to dramatically improve the quality of your AI-generated marketing content? The key is feeding the model with trusted data. And one potentially overlooked source of trusted data is your messaging framework. A messaging framework is a strategic document that outlines the core messages you want to communicate to your target audience. It serves as a guide for all marketing communications and ensures consistency and clarity across various channels and touchpoints. Conveniently, it just happens to include everything generative AI needs to deliver high-quality, on-brand, on-message content. Here's a breakdown of the components I like to include in a messaging framework: 1. Brand Glossary: Define key terms, phrases, and concepts that are central to your brand, industry, and target audience. 2. Brand/Product Naming Conventions: Approved company name, taglines, and usage guidelines. 3. Target Audience Definition: Clearly identifying the intended audience, their demographics, goals, challenges, fears, buying motivations, common objections, preferred communication channels, etc. 4. Value Proposition: A clear statement summarizing the primary benefits your product delivers to your target customers. It communicates the value customers can expect to receive and what differentiates the offering from competitors. 5. Messaging Pillars: The unique selling points that support your value proposition. Pair the top 3-4 prospective customer challenges with your product's key benefits, advantages, features, and proof points. 6. Style Guide: Your preferred writing style, tone, voice, cadence, etc. 7. Messaging Examples: Include samples of your very best ads, emails, articles, social posts, video scripts, etc. Here's the prompt playbook: 1. Use Claude 3 Opus (yeah, the paid version). In my testing, it has consistently produced superior marketing copy to ChatGPT or any other model. 2. Upload your messaging framework. 3. Upload additional reference material. For example, if you want to write an article about how the statistics in a new research study support your value proposition, upload a PDF of the study. 4. Craft a prompt that answers the following questions: