Exploring prompt engineering, reasoning layers, and terminology in chatbot interactions

Loading data from Talking to Chatbots Dataset reddgr/talking-to-chatbots-chats

😎 “When interacting with XYZ, an additional “reasoning layer” is interposed between the input and the output, producing what BCD calls the “reasoning tokens”, that aim at producing better responses that adapt to complex contexts in fields such as software development or scientific research.
🤖 Here is the corrected text:

“When interacting with XYZ, an additional ‘reasoning layer’ is interposed between the input and the output, producing what BCD calls ‘reasoning tokens’ that aim to produce better responses that adapt to complex contexts in fields such as software development or scientific research.”

**Note:** Minor grammatical corrections were made for improved readability.
😎 which corrections?
🤖 The following corrections were made:

1. Changed **”that aim at producing”** to **”that aim to produce”** for conciseness and to align with standard phrasing.

These adjustments improve the flow and readability while maintaining the original meaning.
😎 “‘Language Models Are Few-Shot Learners’ was Originally published by a team of XYZ researchers just a few days before their revolutionary AC5 model was released as a beta version, back in May 2020, more than two years before its public release. To clarify why this context is important, below is a screen capture of a news article from that time:”

Target only the last sentence. Think of other wordings for “to clarify”.
🤖 Here are some alternative wordings for “to clarify” in the last sentence:

1. **”To provide clarity on why this context is important, below is a screen capture of a news article from that time:”**
2. **”To shed light on why this context is important, below is a screen capture of a news article from that time:”**
3. **”To illustrate why this context is important, below is a screen capture of a news article from that time:”**
4. **”To explain why this context is important, below is a screen capture of a news article from that time:”**
😎 less robotic and plain. Be colloquial
🤖 Here are more colloquial alternatives:

1. **”Just to show why this context matters, here’s a screen capture of a news article from that time:”**
2. **”To make it clear why this is important, here’s a screen capture of a news article from back then:”**
3. **”To highlight why this is relevant, check out this screen capture of a news article from that time:”**
😎 “A major practical issue of techniques leveraging in-context-learning, such as CoT, is that, to simply prompt the model before getting an answer, we might need to spend more time crafting prompts for a tool that can only generate text, than the time it would take us to ‘reason’ a solution to the problem, make a decision, and type a text with our own hands.”
🤖 Here is the corrected text:

“A major practical issue with techniques leveraging in-context learning, such as CoT, is that simply prompting the model before getting an answer might require more time spent crafting prompts for a tool that can only generate text than it would take to ‘reason’ a solution, make a decision, and type the text ourselves.”

**Note:** Minor grammatical adjustments were made for clarity and readability.
😎 Something might not be a scam, but its nearly a scam. I’d say it’s ‘scam-bordering’. This term though is not frequently used. Find more that are more common and mean exactly the same. Obviously, the particle ‘scam’ is mandatory
🤖 Here are some more commonly used terms that include the word ‘scam’ and convey a similar meaning:

1. **”Scam-adjacent”**
2. **”Scam-like”**
3. **”Scam-tinted”**
4. **”Scam-esque”**
5. **”Scam-infused”**

These phrases keep the connotation of something being close to a scam without outright labeling it as one.
😎 “The proliferation of clickbaity newsletters and scam-esque books selling chatbot prompts, along with famous media headlines such as “Talking to Chatbots Is Now a $200K Job. So I Applied.” didn’t help build a good reputation for the prompt engineering fad.”
🤖 Here is the corrected text:

“The proliferation of clickbaity newsletters and scam-esque books selling chatbot prompts, along with famous media headlines such as ‘Talking to Chatbots Is Now a $200K Job. So I Applied,’ didn’t help build a good reputation for the prompt engineering fad.”

**Note:** A minor punctuation adjustment was made by placing the comma inside the quotation marks.
😎 “These techniques ranging from rudimentary, to overcomplicated, to nearly esoteric approaches…” Construct this well as a construction that has a from and a to, but we give 3 examples
🤖 “These techniques range from the rudimentary to the overcomplicated, and even to the nearly esoteric…”
😎 “These techniques ranging from rudimentary to overcomplicated, and even to nearly esoteric approaches to talking to chatbots constituted the grounds for many other research studies that eventually would lead to the birth of one of the most controversial (and short-lived) ‘professions’ in the history of the tech industry: Prompt Engineering.”
🤖 Here is the corrected text:

“These techniques, ranging from rudimentary to overcomplicated, and even to nearly esoteric approaches to talking to chatbots, constituted the grounds for many other research studies that would eventually lead to the birth of one of the most controversial (and short-lived) ‘professions’ in the history of the tech industry: Prompt Engineering.”

**Note:** Minor punctuation adjustments were made for clarity.
😎 “The proliferation of clickbaity newsletters and scam-esque books selling chatbot prompts, along with famous media headlines such as “Talking to Chatbots Is Now a $200K Job. So I Applied,” didn’t help build a good reputation for the prompt engineering fad. However, the quick and overwhelming advances in LLMs show us the power of CoT and other techniques labeled as ‘prompt engineering’ did not lie in its immediate use cases for users who send prompts to LLMs but in its potential for building more powerful models and conversational tools that enable new and productive use cases for large language models.”
🤖 Here is the corrected text:

“The proliferation of clickbaity newsletters and scam-esque books selling chatbot prompts, along with famous media headlines such as ‘Talking to Chatbots Is Now a $200K Job. So I Applied,’ didn’t help build a good reputation for the prompt engineering fad. However, the quick and overwhelming advances in LLMs show us that the power of CoT and other techniques labeled as ‘prompt engineering’ did not lie in their immediate use cases for users who send prompts to LLMs, but in their potential for building more powerful models and conversational tools that enable new and productive use cases for large language models.”

**Note:** Minor grammatical corrections were made, including adjusting “its” to “their” to match the plural subject and adding “that” for clarity.
😎 “In prompt engineering terms, we could designate what o1 achieves, and aims to evolve and refine, as a ‘zero-shot CoT,’ but a more sophisticated one than the one that simply adds the ‘let’s think step by step’ before the answer. ”
🤖 Here is the corrected text:

“In prompt engineering terms, we could designate what o1 achieves and aims to evolve and refine as a ‘zero-shot CoT,’ but a more sophisticated one than simply adding ‘let’s think step by step’ before the answer.”

**Note:** Minor punctuation adjustments were made for clarity, and a slight rephrasing was applied to streamline the sentence.
😎 “He still tends to confuse what the bot said with what the user said”. Confuse is not the vest verb. This is intended to convey that “He” tends to think something that the user said was said by the bot and viceversa. Find other verbs or expressions
🤖 Here are some alternative verbs and expressions:

1. **”mixes up”** – “He still tends to mix up what the bot said with what the user said.”
2. **”misattributes”** – “He still tends to misattribute what the bot said to the user and vice versa.”
3. **”mistakes”** – “He still tends to mistake what the bot said for what the user said.”
4. **”gets confused between”** – “He still gets confused between what the bot said and what the user said.”

Hugging Face Dataset Metrics

All the conversation prompts, responses, and metrics are available to download and explore on Hugging Face dataset reddgr/talking-to-chatbots-chats: