Episode 132 of #PodSaveChocolate was inspired by a post in the r/chocolate sub on Reddit. Let’s take a look at what the OP (referring to ChatGPT “advice”) posted, how the community responds, and then ask other LLMs and ChatGPT to chime in and explore what LLMs can teach us about making chocolate.
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My experience using LLMs (e.g., ChatGPT, Google Gemini) to answer questions about making chocolate is that they’re a lot like Goldilocks and the Three Bears, where most of the answers are either “too cold” (sparse) or “too hot” (overwhelming) ⋯ only very rarely are they just right.
This episode of PodSaveChocolate was inspired by a post in the r/chocolate subreddit.
r/chocolate subreddit – The post that inspired this episode
Common mistakes in making chocolate is title of this post. Let’s investigate.
The TL;DR — No. LLMs cannot teach you how to make chocolate. At best they can give you an overview of the process.
And - be careful, which LLM you use, the prompt you use, the “mode” the LLM is in, and even the interface to the LLM all influence the “answers” you will get.
In this episode of #PodSaveChocolate we will continue to explore the limits ⋯ and quirks ⋯ of LLMs when it comes to exploring topics in chocolate.
Goldilocks and the Three LLMs
In this episode I query three LLMs (ChatGPT, Gemini, and Mistral) using four different vectors, or points of entry.
This is the prompt I used for each LLM:
I want to make 1 kilo of milk chocolate from already fermented and dried cocoa beans. What are the steps? What equipment do I need? What is a good recipe?
In particular, I am going to point out how two different vectors into ChatGPT, one via a third-party and the other direct, return very different results. I am also going to show how Gemini generates two very different responses based on how you “engage” it.
The results represent very different philosophies about how the designers think about the nature of human-LLM interaction and how the “conversations” are mediated and moderated.
Why?
Because what you ask (the “prompt”), which LLM you ask, and the vector you use to get answers, all matter.
Images generated by the SORA/ChatGPT chatbot using the prompt: “Can An LLM Teach You How To Make Chocolate?”
If you have questions or want to comment, you can do so during the episode or, if you are a ChocolateLife member, you can add them in the Comments below at any time.
# Can an LLM Teach You How to Make Chocolate?
## #PodSaveChocolate Ep 132 — Summary
This episode dives into whether large language models (LLMs) like ChatGPT, Google Gemini, and Mistral can actually teach you how to make chocolate. Inspired by a Reddit post in r/chocolate, host Clay Gordon uses the community’s reactions and live audience input to explore the strengths and weaknesses of AI-generated chocolate-making advice.
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## Overview
Episode 132 starts with a Reddit post where someone shares their results of some unidentified AI’s step-by-step instructions for making chocolate. The host dissects this advice, examining what LLMs get right, where they fall short, and how the chocolate community responds. The show is interactive, pulling in Reddit comments and live feedback, and points to further resources at TheChocolateLife.com.
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## Key Themes
### 1. LLMs and Chocolate-Making Advice
- Reviews the original Reddit post featuring ChatGPT’s chocolate-making guide.
- Highlights both the useful aspects and common pitfalls of LLM-generated instructions, like oversimplification or missing context.
- Discusses community feedback, especially where AI misses the mark on technical details like cacao bean handling, tempering, and sourcing.
### 2. Community and Expert Input
- Brings in perspectives from Reddit and live viewers to assess the reliability of AI in specialty food spaces.
- Emphasizes that hands-on experience and sensory skills are crucial—things AI can’t provide.
- Explores the idea that LLMs can supplement, but not replace, real expertise and practice.
### 3. Testing Multiple LLMs
- The host asks other LLMs (Google Gemini, Mistral) the same chocolate-making questions.
- Compares their answers for accuracy, depth, and usefulness.
- Finds that while LLMs are good at providing general frameworks and troubleshooting, they often lack critical details.
### 4. The Limits and Potential of AI in Craft Chocolate
- Discusses the broader implications of using AI to learn complex, hands-on skills.
- Stresses the need for curated, expert-vetted resources for anyone serious about chocolate making.
- Highlights the importance of community, mentorship, and learning by doing.
### 5. Resources and Further Exploration
- Directs viewers to TheChocolateLife.com for more info, resources, and ongoing discussion.
- Encourages engagement through live comments, questions, and sharing experiences.
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## Conclusion
LLMs like ChatGPT can offer helpful overviews and answer basic chocolate-making questions. But they’re no substitute for expert knowledge, hands-on practice, and the wisdom of the chocolate-making community. The episode is both a critique and exploration of how AI fits into specialty chocolate, inviting viewers to keep the conversation going and deepen their learning with trusted sources and real-world experimentation.
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