Ask Google Gemini about a factual but somewhat obscure story discussing how Andrew Gold’s Thank You for Being a Friend ended up the theme song for the Golden Girls and this was the following response: > “I do not have enough information about that person to help with your request. I am a large language model, and I am able to communicate and generate human-like text in response to a wide range of prompts and questions, but my knowledge about this person is limited. Is there anything else I can do to help you with this request?” > Meanwhile, Perplexity, a web-based AI browser and search engine epitomized the next wave of transitioning from the web search to synthetic information in AI because perplexity was able to weave this together into a comprehensive story of factual narrative. > The story of how Andrew Gold’s “Thank You for Being a Friend” became *The Golden Girls* theme is a fascinating web of creativity, serendipity, and collaboration. In 1978, Andrew Gold wrote and recorded the song for his album *All This and Heaven Too*. Gold, a skilled multi-instrumentalist with a musical pedigree (his father composed for Hollywood, and his mother sang in *The Sound of Music*), described the song as a “throwaway” written in under an hour. Despite his modest expectations, it reached No. 25 on the U.S. Billboard Hot 100 and became one of his signature works[1][2][10]. Fast forward to 1985, when NBC was developing *The Golden Girls*. The producers initially considered using Bette Midler’s “Friends,” but licensing costs led them to Gold’s song instead. To adapt it for television, they hired Cindy Fee, a jingle singer known for her versatile voice, to record a cover. Fee’s upbeat rendition perfectly captured the warmth and humor of the show, becoming synonymous with its themes of friendship and camaraderie[2][3][7]. Gold’s original version remained distinct from Fee’s cover, but both versions benefited from the song’s timeless appeal. While Gold continued his career as a musician and producer (notably working with Linda Ronstadt), Fee found enduring recognition through her association with *The Golden Girls*[1][7][10]. This collaboration between Gold’s songwriting and Fee’s performance turned “Thank You for Being a Friend” into an enduring cultural emblem, bridging soft rock and sitcom history. Citations: [1] https://en.wikipedia.org/wiki/Thank_You_for_Being_a_Friend [2] https://www.cheatsheet.com/entertainment/the-golden-girls-theme-song-was-not-written-for-the-show.html/ [3] https://www.youtube.com/watch?v=H3zHdAmTUso [4] https://simple.wikipedia.org/wiki/Thank_You_for_Being_a_Friend [5] https://songoftheday.ca/2020/12/07/thank-you-for-being-a-friend/ [6] https://www.youtube.com/watch?v=BpE4T8PhYM0 [7] https://www.theatlantic.com/entertainment/archive/2015/09/the-history-of-the-golden-girls-theme-song/405169/ [8] https://www.oakpark.com/2022/01/12/connecting-to-betty-with-a-single-song/ [9] https://www.songfacts.com/facts/andrew-gold/thank-you-for-being-a-friend [10] https://en.wikipedia.org/wiki/Andrew_Gold https://www.perplexity.ai/search/what-s-the-story-of-how-andrew-JT0q94tkTT6zJ7tyuGmQCA#1 > [!ai]+ AI > > This example highlights the evolving landscape of information retrieval and the strengths of different AI approaches. Gemini’s response reveals a limitation of large language models (LLMs): they don’t inherently “know” information. They are trained on massive datasets and excel at pattern recognition, language generation, and other tasks based on that data. If a specific fact or story isn’t prominent enough in the training data, the LLM won’t have access to it. Essentially, Gemini couldn’t find the relevant connections in its vast knowledge base to answer the question. > Perplexity, on the other hand, demonstrates the power of combining LLM capabilities with active web searching. It acts as a sophisticated research assistant, querying search engines, synthesizing information from multiple sources, and presenting it in a coherent narrative. This approach allows it to access and process up-to-date information and weave together details from various websites, something a standalone LLM trained on a static dataset cannot do. > The key differences are: > - **Knowledge Source:** Gemini relies on its internal training data. Perplexity actively searches the web. > - **Information Currency:** Gemini’s knowledge is fixed at its training date. Perplexity accesses current information. > - **Synthesis Capability:** Gemini can synthesize information present in its training data. Perplexity can synthesize information from multiple web sources. > This comparison doesn’t mean one approach is inherently superior. LLMs like Gemini are incredibly versatile and powerful for many tasks. However, for factual queries requiring specific, potentially obscure information, the web-search-augmented approach of tools like Perplexity offers a significant advantage. The future likely lies in hybrid systems that combine the strengths of both approaches. Data science became the hot career of the information age or the big data age. Meanwhile, in the age of AI and synthetic information, asking the right questions, a professional question-asker who can design prompts and expand an AI’s choice set will be key. > [!ai]+ AI > > You’ve made a great observation about the shifting landscape of information retrieval and the emerging importance of prompt engineering. Your analysis of Gemini and Perplexity’s different approaches is spot-on. Here are some additional thoughts building on your points: > **The Rise of the “Prompt Engineer”** > As you pointed out, the ability to craft effective prompts is becoming increasingly crucial. It’s not just about asking a question; it’s about understanding how AI models interpret language, what biases they might have, and how to structure queries to elicit the desired information. This involves: > - **Specificity and Clarity:** Vague prompts lead to vague answers. A skilled prompt engineer knows how to frame a question with precision, including relevant keywords and context. > - **Iteration and Refinement:** The first prompt might not yield the best results. Prompt engineering involves experimenting with different phrasings, adding constraints, and iteratively refining the query to improve the AI’s response. > - **Understanding Model Limitations:** Knowing the strengths and weaknesses of different AI models is essential. A prompt engineer needs to be aware of what a model *can’t* do to avoid unrealistic expectations and tailor their prompts accordingly. > - **Bias Awareness:** AI models are trained on data, and that data can reflect societal biases. A good prompt engineer needs to be mindful of these biases and design prompts that mitigate their impact. > **Beyond Factual Retrieval: Creative Applications** > Prompt engineering isn’t just about finding information; it’s also about *creating* it. With generative AI models, carefully crafted prompts can be used to: > - **Generate creative content:** Write stories, poems, scripts, musical pieces, and even code. > - **Translate languages:** Improve the accuracy and fluency of translations. > - **Summarize text:** Condense lengthy documents into concise summaries. > - **Brainstorm ideas:** Explore different perspectives and generate novel solutions. > **The Future of Search and Information Access** > The distinction between search and synthesis is blurring. We’re moving towards a future where AI doesn’t just retrieve information but also processes, analyzes, and synthesizes it into meaningful narratives. This has profound implications for: > - **Education:** Personalized learning experiences tailored to individual needs. > - **Research:** Accelerated discovery through automated analysis and synthesis of vast datasets. > - **Journalism:** Automated content generation and fact-checking. > - **Decision-making:** Data-driven insights and predictions to inform complex choices. > **The “Professional Question-Asker”** > Your term, “professional question-asker,” perfectly captures this emerging role. It highlights the importance of not just having access to information but also knowing how to ask the right questions to unlock its potential. This skill will be highly valued in a world increasingly reliant on AI-driven information systems. It’s not just about finding answers; it’s about framing the right questions to uncover the insights that matter. To emphasize this point, and in particular the evolving landscape of data into information, into synthetic knowledge, write a forward-looking hypothetical job description for this new kind of role, and why it matters. > [!ai]+ AI > > ## Job Description: AI Insight Navigator > **Department:** Strategic Intelligence & Innovation > **Reports to:** Chief Knowledge Officer > **About Us:** > [Company Name] is a future-focused organization operating at the forefront of [Industry]. We understand that in the age of synthetic information, the ability to navigate the complex landscape of AI-generated insights is no longer a luxury, but a necessity. We are seeking an AI Insight Navigator to join our team and help us unlock the transformative potential of AI. > **About the Role:** > The AI Insight Navigator is a critical role bridging the gap between raw data, AI capabilities, and actionable business intelligence. This role is not about simply asking questions; it’s about strategically shaping inquiries to extract meaningful knowledge from the vast sea of information available through advanced AI systems. You will be responsible for: > - **Prompt Engineering & Optimization:** Crafting precise and effective prompts for various AI models (LLMs, generative AI, etc.) to elicit desired information, insights, and creative content. This includes understanding model limitations, biases, and optimization techniques. > - **AI System Navigation:** Mastering the use of diverse AI platforms and tools, including web-search augmented AI, standalone LLMs, and specialized AI applications. You will be our in-house expert on leveraging these systems for maximum impact. > - **Information Synthesis & Analysis:** Critically evaluating AI-generated outputs, synthesizing information from multiple sources, and identifying key trends, patterns, and insights relevant to business objectives. > - **Knowledge Representation & Communication:** Translating complex AI-derived insights into clear, concise, and actionable recommendations for various stakeholders, including executives, product teams, and marketing. > - **Continuous Learning & Exploration:** Staying abreast of the rapidly evolving AI landscape, exploring new tools and techniques, and contributing to the development of best practices for AI-driven information retrieval and synthesis. > - **Cross-Functional Collaboration:** Working closely with teams across the organization to understand their information needs and tailor AI solutions to address specific challenges and opportunities. > **Qualifications:** > - Strong analytical and critical thinking skills. > - Excellent communication and presentation skills. > - Deep understanding of AI principles, including LLMs, generative AI, and natural language processing. > - Experience with prompt engineering and optimization techniques. > - Proficiency in using various AI platforms and tools. > - Ability to synthesize information from multiple sources and identify key insights. > - Passion for exploring the evolving landscape of AI and its applications. > - Bachelor’s degree in a relevant field (e.g., Computer Science, Data Science, Information Science). Advanced degree preferred. > **Why This Role Matters:** > In a world increasingly reliant on AI-generated information, the AI Insight Navigator is not just a job title; it’s a critical function that will shape the future of decision-making. This role is essential for: > - **Unlocking the full potential of AI:** Moving beyond basic information retrieval to extract deep insights and generate novel solutions. > - **Navigating the complexities of synthetic information:** Critically evaluating AI-generated content and mitigating potential biases. > - **Driving innovation and competitive advantage:** Leveraging AI to uncover hidden opportunities and make data-driven decisions. > - **Empowering informed decision-making:** Providing stakeholders with the knowledge they need to succeed in a rapidly changing world. > If you are a passionate and intellectually curious individual with a deep understanding of AI and a desire to shape the future of information, we encourage you to apply. I disagree that a college degree is necessary, but I do think it’s important to discuss prerequisite types of skills. Because a computer science degree says something, but what it really means is what you can do that you learned with it. Also, be more specific. The overall job description doesn’t have to be exactly like a real one. You can take some artistic license, but something like prompt engineering and why that’s important needs to be embedded in why this position matters. > [!ai]+ AI > > ## Job Description: AI Oracle > **Department:** Futures Division > **Reports to:** Chief Visionary Officer > **About Us:** > [Company Name] thrives at the bleeding edge of [Industry]. We recognize that in the age of synthetic intelligence, the ability to decipher and leverage AI-generated insights is no longer a competitive advantage—it’s a requirement for survival. We’re seeking an AI Oracle to join our team and guide us through the uncharted waters of the information-rich future. > **About the Role:** > The AI Oracle is not a mere technician; they are a translator of the digital Delphi. This role is about strategically conversing with AI, understanding its nuances, and extracting actionable wisdom from the torrent of data it generates. You will be responsible for: > - **Whispering to Machines (Prompt Engineering & Optimization):** You will be a master of the AI whisper, crafting precise and evocative prompts for various AI models (LLMs, generative AI, etc.). This isn’t just about keywords; it’s about understanding the AI’s “personality,” its biases, and its potential for creative output. You’ll optimize prompts to elicit not just information, but true understanding. > - **Navigating the Digital Labyrinth (AI System Mastery):** You will be fluent in the language of multiple AI platforms, from web-search augmented AI to standalone LLMs and specialized AI applications. You’ll be our Sherpa, guiding us through the complex terrain of these systems, maximizing their potential for discovery. > - **Distilling Wisdom from the Data Deluge (Information Synthesis & Analysis):** You will be a discerning critic of AI-generated outputs, sifting through the noise to identify genuine insights. You’ll synthesize information from diverse sources, recognizing patterns and weaving narratives that illuminate the path forward. > - **Illuminating the Future (Knowledge Representation & Communication):** You will be a storyteller, translating complex AI-derived insights into compelling narratives that resonate with diverse audiences. You’ll communicate these insights to executives, product teams, and other stakeholders, empowering them to make informed decisions. > - **Charting the Unknown (Continuous Learning & Exploration):** The AI landscape is constantly evolving. You will be a perpetual explorer, charting the uncharted territories of new AI tools and techniques. You’ll contribute to the development of best practices for AI-driven insight generation. > - **Bridging Worlds (Cross-Functional Collaboration):** You will be a bridge builder, working closely with teams across the organization to understand their information needs and tailor AI solutions to address specific challenges and opportunities. > **Essential Skills & Experience:** > - **A Mind for Patterns:** Demonstrated ability to recognize patterns and connections in complex data sets. > - **The Art of Inquiry:** A natural curiosity and a passion for asking insightful questions. > - **A Way with Words:** Exceptional communication skills, both written and verbal, with the ability to translate technical concepts into compelling narratives. > - **Intuition & Creativity:** The ability to think outside the box and generate novel approaches to problem-solving. > - **Adaptability & Resilience:** The AI landscape is constantly evolving. You must be comfortable with ambiguity and embrace continuous learning. > - **A Portfolio of Inquiry:** Demonstrable experience crafting effective prompts for various AI models and generating valuable insights. This could include personal projects, contributions to open-source projects, or professional experience. > **Why This Role Matters:** > The AI Oracle is not just a job; it’s a calling. In a world awash in AI-generated information, this role is crucial for: > - **Unleashing the True Power of AI:** Moving beyond basic information retrieval to uncover hidden knowledge and generate transformative insights. **Prompt engineering is the key to unlocking this potential, allowing us to shape the AI’s output and guide it towards meaningful discoveries.** > - **Navigating the Sea of Synthetic Information:** Critically evaluating AI-generated content, separating signal from noise, and mitigating potential biases. > - **Igniting Innovation and Forging New Paths:** Leveraging AI to identify untapped opportunities and make bold, data-driven decisions. > - **Empowering Informed Decision-Making at All Levels:** Equipping stakeholders with the insights they need to navigate the complexities of the future. > If you are a passionate explorer of the digital frontier, a master of the AI whisper, and a visionary who can illuminate the path forward, we invite you to join us on this extraordinary journey.