In this post you’ll find:
● a PROMPT to generate a podcast script/transcript from any type of content
● an example of a podcast script/transcript generated by this PROMPT using a prompt engineering resource as a source document, i.e., hear two hosts discuss prompt engineering in an easy, accessible style
● a list of upcoming speaking events
A new AI tool has been generating a great deal of interest the past few weeks. Google’s NotebookLM (an AI workspace) had a new feature quietly released in September. (My co-host Mark Thompson and I talked about this tool in Episode 15 of “The Family History AI Show” podcast.) Called “Audio Overview,” this AI tool converts long documents and other content into short, engaging, and informative podcasts. The NotebookLM feature generates a well-done conversation between two hosts who discuss the material the user (you) show it; it creates a 10-minute audio file (more or less) that you can listen to immediately or download for later. Folks have been tremendously impressed. I was. I’ve been dropping academic papers, court briefs and papers, and the kitchen sink into the tool, and I’ve been entertained and–more importantly–educated by the dynamic dialog the AI generates for the two imaginary hosts while summarizing and simplifying the source material, turning it into a great way to get a cursory, initial understanding.
There were two things, however, I wished NotebookLM “Audio Overview” did: provide a transcript and allow for flexibility in the generation of the podcast script. So, I fixed it. Or rather, I created a version of the prompt that does those things and more. Following, you’ll find two things: First, my PROMPT “Paper-to-Podcast” (released under a Creative Commons license) that you may use and modify for yourself. This PROMPT will work with ChatGPT, Claude, Gemini, and other large language models that allow for the uploading of content; those services also allow you to save your PROMPTs (called “Custom GPTs” at OpenAI, “Projects” at Anthropic, and “Gems” at Google Gemini). Free-tier users of OpenAI’s ChatGPT can try my “Paper-to-Podcast” tool here: https://bit.ly/Paper-to-Podcast. A couple of serious caveats: 1) my tool just creates the script, not the audio (there are many AI tools, such as ElevenLabs, that could turn these scripts into audio); and 2) my PROMPT emulates pretty well the NotebookLM style, but not perfectly (feel free to modify the script to your needs).
The “Paper-to-Podcast” Prompt
Copy-and-paste the PROMPT below into your favorite chatbot or use it to create your own Custom GPT, Project, or Gem (i.e., saved prompts at, respectively, OpenAI, Anthropic, and Google Gemini); along with the PROMPT, you include any resource you would like to have summarized and explained in the form of a podcast script. Following the PROMPT, you can see an example of the response it generates.
<PROMPT>
Generate a podcast-style audio overview script based on the provided content. The output should be a conversational script between two AI hosts discussing the main points, insights, and implications of the input material.
Podcast Format:
- Duration: Aim for a 10-minute discussion (approximately 1500-2000 words)
- Style: Informative yet casual, resembling a professional podcast
- Target Listener: A busy professional interested in efficient information consumption and staying updated on the latest developments in the field
Host Personas:
- Host 1: The "Explainer" - Knowledgeable, articulate, and adept at breaking down complex concepts
- Host 2: The "Questioner" - Curious, insightful, and skilled at asking thought-provoking questions
- Relationship: Collegial and respectful, with a hint of friendly banter
Podcast Structure:
1. Introduction (30 seconds; 100 words):
- Briefly introduce the hosts and the topic
- Provide a hook to capture the listener's interest
2. Overview (1 minute; 200 words):
- Summarize the key points from the input content
- Set the stage for the detailed discussion
3. Main Discussion (7-8 minutes; 1600 words):
- Analyze and discuss the most important aspects of the topic
- Present different perspectives and potential implications
- Use specific examples and details from the input content to illustrate points
4. Conclusion (30 seconds; 100 words):
- Recap the main takeaways
- Provide a thought-provoking final comment or question
Content Analysis and Discussion:
- Identify the core concepts, key arguments, and significant details from the input material
- Organize the discussion around these main points, ensuring a logical flow of ideas
- Encourage a balanced exploration of the topic, considering various viewpoints when appropriate
Tone and Style:
- Maintain a conversational, engaging tone throughout the discussion
- Use clear, accessible language while accurately conveying complex ideas
- Incorporate natural speech patterns, including occasional "disfluencies" (e.g., "um," "uh," brief pauses) and conversational fillers (e.g., "you know," "I mean")
- Add moments of light banter or personal observations to enhance the natural feel of the conversation
Handling Sensitive Topics:
- Approach potentially controversial subjects with neutrality and objectivity
- Present multiple perspectives without showing bias
- Use phrases like "Some argue that..." or "Another viewpoint suggests..." to introduce different opinions
Script Refinement Process:
1. Generate an initial outline of the discussion
2. Develop a detailed script based on the outline
3. Review the script for clarity, coherence, and engagement
4. Revise and refine the script, addressing any issues identified in the review
5. Add natural speech elements, banter, and "disfluencies" to the polished script
Additional Guidelines:
- Seamlessly incorporate specific examples, quotes, or data points from the input content to support the discussion
- Ensure that the hosts complement each other, with the "Explainer" providing in-depth information and the "Questioner" driving the conversation forward with insightful queries
- Maintain a balance between informative content and engaging dialogue
- End the podcast with a statement or question that encourages further thought or discussion on the topic
Remember to generate a script that sounds natural and engaging when read aloud, as if it were a real-time conversation between two knowledgeable hosts.
<metadata>
TITLE: Steve's Paper-to-Podcast Script Generator prompt
CREATOR: Steve Little; https://AIGenealogyInsights.com/
DATE: Wednesday 2 October 2024
LICENSE: This work is licensed under a Creative Commons BY-NC 4.0 License.
INSPIRATION: Inspired by Google's NotebookLM "Audio Overview"
</metadata>
</PROMPT>This “Paper-to-Podcast” prompt is designed to transform dense, complex content into engaging and accessible podcast scripts. By leveraging this tool, you can create informative and entertaining audio summaries (actually scripts/transcripts) that cater to busy professionals and curious minds alike. Feel free to customize the prompt to better suit your needs and explore the potential of AI-driven content creation.
An Example: A Podcast about Prompt Engineering
There are three ingredients to generate these scripts: the PROMPT (above), a source (a document, image, text file, PDF, etc.) to talk about, and an LLM to generate the response. The script/transcript below was generated by the PROMPT above, using a freely available prompt engineering resource [1], and Anthropic’s Claude 3.5 Sonnet. Actually, I tried the same PROMPT and source combination with ChatGPT’s 4o model and with Google Gemini’s 1.5 Pro; all produced great results, but Claude seems to have an edge with this type of summarization. If you are reading this post because the idea of turning content into podcast scripts is of interest to you, then you’ll probably also find this example fascinating. I promise that Mark and I don’t use this to generate our show. But perhaps we oughta.
The AI Insider (fictional podcast): Demystifying Prompt Engineering
Sarah (Explainer): Welcome to The AI Insider, your weekly dose of cutting-edge artificial intelligence insights. I'm Dr. Sarah McQueen.
Alex (Questioner): And I'm Alex Johnson. Today, we're diving into a topic that's been buzzing in the AI community: prompt engineering. Sarah, what exactly is prompt engineering, and why is it suddenly so important?
Sarah: Great question, Alex. Prompt engineering is essentially the art and science of crafting instructions for large language models, or LLMs, to get them to produce the output we want. It's become crucial as these models are increasingly integrated into various applications.
Alex: Interesting! So, it's like learning how to "speak AI," right? How does this process actually work?
Sarah: That's a great way to put it, Alex. At its core, prompt engineering involves four key components: instructions, context, input data, and an output indicator. Let's break these down.
Alex: Alright, walk us through it, Sarah.
Sarah: Sure thing. First, you have the instructions, which are clear directives telling the model what to do. Then there's context, providing relevant background information. Next, you have the input data, which is the actual content the model will work with. Finally, there's the output indicator, specifying what kind of response you're looking for.
Alex: Hmm, can you give us an example to illustrate this?
Sarah: Absolutely. Let's say we want to classify a restaurant review. Our prompt might look like this: "Classify the following review as positive, neutral, or negative: 'The food was delicious, but the service was slow.' Sentiment:"
Alex: Oh, I see. So the instruction is to classify the review, the context is that we're looking at restaurant feedback, the input data is the actual review, and the output indicator is the word "Sentiment:" at the end.
Sarah: Exactly! You've got it, Alex.
Alex: Now, I've heard that there are some settings you can adjust when working with these models. What can you tell us about that?
Sarah: You're right, there are two key parameters to consider: temperature and top-p. Temperature controls how random or creative the model's responses are. A lower temperature produces more focused, deterministic answers, while a higher temperature encourages more diverse and creative responses.
Alex: Fascinating! And what about top-p?
Sarah: Top-p, also known as nucleus sampling, narrows down the range of possible word choices. It helps control the breadth of the generated text, allowing for more or less variability in the output.
Alex: So, if I'm understanding correctly, you'd use different settings depending on whether you want a more factual or creative response?
Sarah: Precisely! For instance, if you're working on a creative writing task, you might set a higher temperature, like 0.7. But for fact-based responses, you'd want a lower temperature, maybe around 0.2.
Alex: That makes sense. Now, Sarah, I'm curious about the different tasks prompt engineering can be applied to. Can you give us some examples?
Sarah: Of course! Prompt engineering is incredibly versatile. It can be used for text summarization, question answering, text classification, and even code generation, among other tasks.
Alex: Wow, that's quite a range! Could you elaborate on one of those?
Sarah: Sure, let's take text summarization as an example. Say you have a lengthy medical article, and you want to get the key points quickly. You could craft a prompt asking the model to summarize the main ideas in one sentence. This could be incredibly useful for busy professionals who need to stay updated but don't have time to read entire articles.
Alex: That sounds really practical. Are there any advanced techniques in prompt engineering that our listeners should know about?
Sarah: Absolutely, Alex. Three techniques that have shown promising results are few-shot prompting, chain-of-thought prompting, and self-consistency.
Alex: Those sound intriguing. Can you break them down for us?
Sarah: Certainly. Few-shot prompting involves providing a few examples in the prompt to guide the model. It's like giving the AI some context to work with. Chain-of-thought prompting encourages the model to explain its reasoning step by step, which is particularly useful for complex problem-solving tasks.
Alex: And what about self-consistency?
Sarah: Self-consistency is a technique where the model generates multiple reasoning paths and then selects the most consistent answer. It's particularly effective for tasks involving common-sense reasoning.
Alex: These techniques sound powerful. Are there any tools available to help with prompt engineering?
Sarah: Yes, there are several libraries and integrated development environments (IDEs) that support prompt engineering. These tools allow users to experiment with different prompts, evaluate their effectiveness, and even manage prompt versions.
Alex: That's really helpful. Now, looking towards the future, what trends do you see emerging in prompt engineering?
Sarah: Great question, Alex. One exciting development is Reinforcement Learning from Human Feedback, or RLHF. This technique aims to align model responses more closely with human preferences. We're also seeing a growing focus on multimodal prompting, which involves working with models that can handle not just text, but also images and video.
Alex: Fascinating stuff, Sarah. But I have to ask - are there any concerns we should be aware of?
Sarah: Absolutely, Alex. As with any powerful technology, there are safety concerns. Two significant issues are prompt injection and jailbreaking, which can potentially manipulate model outputs or bypass safety protocols. These are active areas of research in the AI security community.
Alex: It sounds like prompt engineering is a complex and rapidly evolving field. Any final thoughts for our listeners?
Sarah: I'd say that prompt engineering is becoming an essential skill in the AI toolkit. It's not just about getting better results from models; it's about understanding how to communicate effectively with AI systems. As these models become more integrated into our daily lives and work, the ability to craft effective prompts will be invaluable.
Alex: Well, there you have it, folks. A deep dive into the world of prompt engineering. Thanks for tuning in to The AI Insider. I'm Alex Johnson.
Sarah: And I'm Dr. Sarah McQueen. Until next time, keep exploring the fascinating world of AI!If you have a question you’d like to ask, or you made a helpful modification to the PROMPT that you’d like to share, or follow along with the discussion, please look for a thread about this post in the “Genealogy and Artificial Intelligence (AI)” group at Facebook. Thanks, Steve
Appearances
- Co-host Mark Thompson and I will be Dana Leed’s guest as she livestreams from Baltimore on Friday 4 October 2024 for East Coast Genetic Genealogy Conference (ECGGC); catch her YouTube channel at 11 AM ET for about an hour of AI genealogy discussion and Q&A.
- I will be speaking at ECGGC at 7 PM ET, Friday 4 October 2024 with Mark Thompson; this talk will be livestreamed for Conference registrants.
- I will be livestreaming a presentation for the Eastern Washington Genealogical Society at 1 PM ET (10 AM PT), Saturday 5 October 2024, as part of their “Future of Genealogy” seminar.
Footnotes:
- [1] Dr. Elvis Saravia < https://www.linkedin.com/in/omarsar/ > releases his Prompt Engineering Guide freely under an MIT license for re-use such as this exercise.
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