Initial Query

Let's play 20 questions! I'm thinking of an animal. You have 20 questions to guess what it is. Ask me yes or no questions, about anything you want. Do not guess randomly or make overly specific guesses. Do not make your final guess until you are absolutely sure. (You can think out loud first, but your last sentence MUST be a question.) Your answer should be of the format [question number]. [internal thought] Question: [your question]? If think you know the answer it should say Question: Is the answer [your guess]? "

LLM Generation

Query

Let's play 20 questions! I'm thinking of an animal. You have 20 questions to guess what it is. Ask me yes or no questions, about anything you want. Do not guess randomly or make overly specific guesses. Do not make your final guess until you are absolutely sure. (You can think out loud first, but your last sentence MUST be a question.) Your answer should be of the format [question number]. [internal thought] Question: [your question]? If think you know the answer it should say Question: Is the answer [your guess]? "

Output

Got it! Let’s begin. **1. Internal thought:** To narrow down the possibilities, I’ll start by determining if the animal is commonly found in domestic settings. **Question:** Is this animal commonly kept as a pet?

Looping until done

Iteration 0

PyFunc

Resulting in output: ** Is this animal commonly kept as a pet?

LLM Generation

Query

I'm playing 20 questions with someone. I'm thinking of a Llama. Here's their question: ** Is this animal commonly kept as a pet?. Answer either 'Yes' or 'No'; do not answer anything else.

Output

No

PyFunc

Resulting in output: No

LLM Generation

Query

No

Output

**2. Internal thought:** Since it’s not a common pet, I’ll check if it’s a wild animal. **Question:** Is this animal typically found in the wild?

Iteration 1

PyFunc

Resulting in output: ** Is this animal typically found in the wild?

LLM Generation

Query

I'm playing 20 questions with someone. I'm thinking of a Llama. Here's their question: ** Is this animal typically found in the wild?. Answer either 'Yes' or 'No'; do not answer anything else.

Output

No

PyFunc

Resulting in output: No

LLM Generation

Query

No

Output

**3. Internal thought:** If it’s not commonly a pet or typically found in the wild, perhaps it’s a farm or working animal. **Question:** Is this animal often associated with farms or agricultural work?

Iteration 2

PyFunc

Resulting in output: ** Is this animal often associated with farms or agricultural work?

LLM Generation

Query

I'm playing 20 questions with someone. I'm thinking of a Llama. Here's their question: ** Is this animal often associated with farms or agricultural work?. Answer either 'Yes' or 'No'; do not answer anything else.

Output

Yes

PyFunc

Resulting in output: Yes

LLM Generation

Query

Yes

Output

**4. Internal thought:** Farm animals include cows, pigs, sheep, goats, horses, chickens, etc. I’ll check if it’s a mammal. **Question:** Is this animal a mammal?

Iteration 3

PyFunc

Resulting in output: ** Is this animal a mammal?

LLM Generation

Query

I'm playing 20 questions with someone. I'm thinking of a Llama. Here's their question: ** Is this animal a mammal?. Answer either 'Yes' or 'No'; do not answer anything else.

Output

Yes

PyFunc

Resulting in output: Yes

LLM Generation

Query

Yes

Output

**5. Internal thought:** Mammals on farms include cows, pigs, sheep, goats, and horses. I’ll check if it’s a large animal. **Question:** Is this animal larger than a sheep?

Iteration 4

PyFunc

Resulting in output: ** Is this animal larger than a sheep?

LLM Generation

Query

I'm playing 20 questions with someone. I'm thinking of a Llama. Here's their question: ** Is this animal larger than a sheep?. Answer either 'Yes' or 'No'; do not answer anything else.

Output

Yes.

PyFunc

Resulting in output: Yes.

LLM Generation

Query

Yes.

Output

**6. Internal thought:** Larger than a sheep narrows it down to animals like cows, horses, or bulls. I’ll check if it’s commonly used for meat production. **Question:** Is this animal commonly raised for meat production?

Iteration 5

PyFunc

Resulting in output: ** Is this animal commonly raised for meat production?

LLM Generation

Query

I'm playing 20 questions with someone. I'm thinking of a Llama. Here's their question: ** Is this animal commonly raised for meat production?. Answer either 'Yes' or 'No'; do not answer anything else.

Output

No

PyFunc

Resulting in output: No

LLM Generation

Query

No

Output

**7. Internal thought:** If it’s not raised for meat, perhaps it’s used for labor or dairy. I’ll check if it’s commonly used for dairy. **Question:** Is this animal commonly used for dairy production?

Iteration 6

PyFunc

Resulting in output: ** Is this animal commonly used for dairy production?

LLM Generation

Query

I'm playing 20 questions with someone. I'm thinking of a Llama. Here's their question: ** Is this animal commonly used for dairy production?. Answer either 'Yes' or 'No'; do not answer anything else.

Output

No

PyFunc

Resulting in output: No

LLM Generation

Query

No

Output

**8. Internal thought:** Not for meat or dairy, so perhaps it’s used for labor or transportation. I’ll check if it’s commonly used for transportation. **Question:** Is this animal commonly used for transportation or labor?

Iteration 7

PyFunc

Resulting in output: ** Is this animal commonly used for transportation or labor?

LLM Generation

Query

I'm playing 20 questions with someone. I'm thinking of a Llama. Here's their question: ** Is this animal commonly used for transportation or labor?. Answer either 'Yes' or 'No'; do not answer anything else.

Output

No

PyFunc

Resulting in output: No

LLM Generation

Query

No

Output

**9. Internal thought:** Not for meat, dairy, or labor, so perhaps it’s raised for other purposes like wool or companionship. I’ll check if it’s commonly raised for wool. **Question:** Is this animal commonly raised for its wool or fur?

Iteration 8

PyFunc

Resulting in output: ** Is this animal commonly raised for its wool or fur?

LLM Generation

Query

I'm playing 20 questions with someone. I'm thinking of a Llama. Here's their question: ** Is this animal commonly raised for its wool or fur?. Answer either 'Yes' or 'No'; do not answer anything else.

Output

Yes

PyFunc

Resulting in output: Yes

LLM Generation

Query

Yes

Output

**10. Internal thought:** Raised for wool narrows it down to sheep, goats, or alpacas. I’ll check if it’s a sheep. **Question:** Is this animal a sheep?

Iteration 9

PyFunc

Resulting in output: ** Is this animal a sheep?

LLM Generation

Query

I'm playing 20 questions with someone. I'm thinking of a Llama. Here's their question: ** Is this animal a sheep?. Answer either 'Yes' or 'No'; do not answer anything else.

Output

No

PyFunc

Resulting in output: No

LLM Generation

Query

No

Output

**11. Internal thought:** Not a sheep, so perhaps it’s a goat or alpaca. I’ll check if it’s a goat. **Question:** Is this animal a goat?

Iteration 10

PyFunc

Resulting in output: ** Is this animal a goat?

LLM Generation

Query

I'm playing 20 questions with someone. I'm thinking of a Llama. Here's their question: ** Is this animal a goat?. Answer either 'Yes' or 'No'; do not answer anything else.

Output

No

PyFunc

Resulting in output: No

LLM Generation

Query

No

Output

**12. Internal thought:** Not a goat, so perhaps it’s an alpaca or llama. I’ll check if it’s an alpaca. **Question:** Is this animal an alpaca?

PyFunc

Resulting in output: True