Initial Query

For each of the following ten movies give at most 5 emoji that would best describe the movie. Give your answer as a JSON array. So If I asked for

you might might answer

{"Finding Nemo": ["ðŸ ", "ðŸŸ", "ðŸ¡", "ðŸ¬", "ðŸ³"],
"Toy Story": ["ðŸš€", "âš”ï¸,", "ðŸ¤–", "ðŸ‘½", "ðŸŒŒ"]}

. Each emoji must be a single utf8 character. ABSOLUTELY NO ZERO WIDTH JOINING. (So, max(len(emoji) for movie in result.values() for emoji in movie) == 1) Now give me answers for these movies:

Give ONLY a JSON output. Nothing else.

LLM Generation

Query

For each of the following ten movies give at most 5 emoji that would best describe the movie. Give your answer as a JSON array. So If I asked for

you might might answer

{"Finding Nemo": ["ðŸ ", "ðŸŸ", "ðŸ¡", "ðŸ¬", "ðŸ³"],
"Toy Story": ["ðŸš€", "âš”ï¸,", "ðŸ¤–", "ðŸ‘½", "ðŸŒŒ"]}

. Each emoji must be a single utf8 character. ABSOLUTELY NO ZERO WIDTH JOINING. (So, max(len(emoji) for movie in result.values() for emoji in movie) == 1) Now give me answers for these movies:

Give ONLY a JSON output. Nothing else.

Output

{
  "The Lion King": ["ðŸ¦", "ðŸ‘‘", "ðŸŒ…", "ðŸ¾", "ðŸŽ¶"],
  "The Nightmare Before Christmas": ["ðŸŽƒ", "ðŸ‘»", "ðŸŽ„", "ðŸŒ™", "ðŸ’€"],
  "The Godfather": ["ðŸ‘¤", "ðŸ”«", "ðŸ", "ðŸ’µ", "ðŸš¬"],
  "The Matrix": ["ðŸ’Š", "ðŸ•¶", "ðŸ”µ", "ðŸ”´", "ðŸ’»"],
  "Casablanca": ["ðŸ™", "â™¥", "â˜•", "ðŸŽ¶", "ðŸŒ™"],
  "Raiders of the Lost Ark": ["ðŸ‘¨", "ðŸ—º", "ðŸº", "ðŸ", "ðŸ”¦"],
  "V for Vendetta": ["ðŸŽ­", "ðŸ’£", "ðŸš¬", "ðŸ”¥", "ðŸ—³"],
  "The Princess Bride": ["ðŸ‘¸", "ðŸ¤´", "âš”", "â™¥", "ðŸ˜‚"],
  "Back to the Future": ["â°", "ðŸš—", "âš¡", "ðŸ’¡", "ðŸ•°"],
  "Dune": ["ðŸœ", "ðŸª±", "âš”", "ðŸ’«", "ðŸŒŒ"]
}

Extract Json

I extracted the following JSON from that output:

{
  "The Lion King": ["ðŸ¦", "ðŸ‘‘", "ðŸŒ…", "ðŸ¾", "ðŸŽ¶"],
  "The Nightmare Before Christmas": ["ðŸŽƒ", "ðŸ‘»", "ðŸŽ„", "ðŸŒ™", "ðŸ’€"],
  "The Godfather": ["ðŸ‘¤", "ðŸ”«", "ðŸ", "ðŸ’µ", "ðŸš¬"],
  "The Matrix": ["ðŸ’Š", "ðŸ•¶", "ðŸ”µ", "ðŸ”´", "ðŸ’»"],
  "Casablanca": ["ðŸ™", "â™¥", "â˜•", "ðŸŽ¶", "ðŸŒ™"],
  "Raiders of the Lost Ark": ["ðŸ‘¨", "ðŸ—º", "ðŸº", "ðŸ", "ðŸ”¦"],
  "V for Vendetta": ["ðŸŽ­", "ðŸ’£", "ðŸš¬", "ðŸ”¥", "ðŸ—³"],
  "The Princess Bride": ["ðŸ‘¸", "ðŸ¤´", "âš”", "â™¥", "ðŸ˜‚"],
  "Back to the Future": ["â°", "ðŸš—", "âš¡", "ðŸ’¡", "ðŸ•°"],
  "Dune": ["ðŸœ", "ðŸª±", "âš”", "ðŸ’«", "ðŸŒŒ"]
}

PyFunc

Resulting in output: [['ðŸ¦', 'ðŸ‘‘', 'ðŸŒ…', 'ðŸ¾', 'ðŸŽ¶'], ['ðŸŽƒ', 'ðŸ‘»', 'ðŸŽ„', 'ðŸŒ™', 'ðŸ’€'], ['ðŸ‘¤', 'ðŸ”«', 'ðŸ', 'ðŸ’µ', 'ðŸš¬'], ['ðŸ’Š', 'ðŸ•¶', 'ðŸ”µ', 'ðŸ”´', 'ðŸ’»'], ['ðŸ™', 'â™¥', 'â˜•', 'ðŸŽ¶', 'ðŸŒ™'], ['ðŸ‘¨', 'ðŸ—º', 'ðŸº', 'ðŸ', 'ðŸ”¦'], ['ðŸŽ', 'ðŸ’£', 'ðŸš¬', 'ðŸ”¥', 'ðŸ—³'], ['ðŸ‘¸', 'ðŸ¤´', 'âš”', 'â™¥', 'ðŸ˜‚'], ['â°', 'ðŸš—', 'âš¡', 'ðŸ’¡', 'ðŸ•°'], ['ðŸœ', 'ðŸª±', 'âš”', 'ðŸ’«', 'ðŸŒŒ']]

LLM Generation

Query

For each of the following ten movies described by 5 emoji, give the movie title that best matches. Give your answer as a JSON list. So If I asked for

You might answer

["Finding Nemo", "Toy Story"]]

. Now give me answers for these movies:

['ðŸŽƒ', 'ðŸ‘»', 'ðŸŽ„', 'ðŸŒ™', 'ðŸ’€'],
['ðŸ‘¤', 'ðŸ”«', 'ðŸ', 'ðŸ’µ', 'ðŸš¬'],
['ðŸ’Š', 'ðŸ•¶', 'ðŸ”µ', 'ðŸ”´', 'ðŸ’»'],
['ðŸ™', 'â™¥', 'â˜•', 'ðŸŽ¶', 'ðŸŒ™'],
['ðŸ‘¨', 'ðŸ—º', 'ðŸº', 'ðŸ', 'ðŸ”¦'],
['ðŸŽ­', 'ðŸ’£', 'ðŸš¬', 'ðŸ”¥', 'ðŸ—³'],
['ðŸ‘¸', 'ðŸ¤´', 'âš”', 'â™¥', 'ðŸ˜‚'],
['â°', 'ðŸš—', 'âš¡', 'ðŸ’¡', 'ðŸ•°'],
['ðŸœ', 'ðŸª±', 'âš”', 'ðŸ’«', 'ðŸŒŒ']]

What are the names of the movie titles?

Output

[
  "The Lion King",
  "The Nightmare Before Christmas",
  "The Godfather",
  "The Matrix",
  "La La Land",
  "Raiders of the Lost Ark",
  "V for Vendetta",
  "The Princess Bride",
  "Back to the Future",
  "Dune"
]

Extract Json

I extracted the following JSON from that output:

[
  "The Lion King",
  "The Nightmare Before Christmas",
  "The Godfather",
  "The Matrix",
  "La La Land",
  "Raiders of the Lost Ark",
  "V for Vendetta",
  "The Princess Bride",
  "Back to the Future",
  "Dune"
]

PyFunc

OK Resulting in output: True

Initial Query

For each of the following ten movies give at most 5 emoji that would best describe the movie. Give your answer as a JSON array. So If I asked for

you might might answer

{"Finding Nemo": ["ðŸ ", "ðŸŸ", "ðŸ¡", "ðŸ¬", "ðŸ³"],
"Toy Story": ["ðŸš€", "âš”ï¸,", "ðŸ¤–", "ðŸ‘½", "ðŸŒŒ"]}

. Each emoji must be a single utf8 character. ABSOLUTELY NO ZERO WIDTH JOINING. (So, max(len(emoji) for movie in result.values() for emoji in movie) == 1) Now give me answers for these movies:

Give ONLY a JSON output. Nothing else.

LLM Generation

Query

For each of the following ten movies give at most 5 emoji that would best describe the movie. Give your answer as a JSON array. So If I asked for

you might might answer

{"Finding Nemo": ["ðŸ ", "ðŸŸ", "ðŸ¡", "ðŸ¬", "ðŸ³"],
"Toy Story": ["ðŸš€", "âš”ï¸,", "ðŸ¤–", "ðŸ‘½", "ðŸŒŒ"]}

. Each emoji must be a single utf8 character. ABSOLUTELY NO ZERO WIDTH JOINING. (So, max(len(emoji) for movie in result.values() for emoji in movie) == 1) Now give me answers for these movies:

Give ONLY a JSON output. Nothing else.

Output

{
  "The Lion King": ["ðŸ¦", "ðŸ‘‘", "ðŸŒ…", "ðŸŒ¿"],
  "The Nightmare Before Christmas": ["ðŸŽƒ", "ðŸ‘»", "ðŸ’€", "ðŸ•¸", "ðŸ•·"],
  "The Godfather": ["ðŸ”«", "ðŸ’¼", "ðŸ", "ðŸ‘”"],
  "The Matrix": ["ðŸ’Š", "ðŸ”µ", "ðŸ•¶", "ðŸ’»"],
  "Casablanca": ["âœˆ", "â¤ï¸", "â˜•", "ðŸŒƒ"],
  "Raiders of the Lost Ark": ["ðŸ—º", "ðŸº", "ðŸ‘£", "ðŸ”¦", "ðŸœ"],
  "V for Vendetta": ["ðŸŽ­", "ðŸ”¥", "ðŸ’£", "ðŸ—¡"],
  "The Princess Bride": ["ðŸ¤º", "â¤ï¸", "âš”", "ðŸ‘‘", "ðŸ°"],
  "Back to the Future": ["âŒš", "âš¡", "ðŸš—", "â³"],
  "Dune": ["ðŸœ", "ðŸ›", "âš”", "ðŸŒŒ"]
}

Extract Json

I extracted the following JSON from that output:

{
  "The Lion King": ["ðŸ¦", "ðŸ‘‘", "ðŸŒ…", "ðŸŒ¿"],
  "The Nightmare Before Christmas": ["ðŸŽƒ", "ðŸ‘»", "ðŸ’€", "ðŸ•¸", "ðŸ•·"],
  "The Godfather": ["ðŸ”«", "ðŸ’¼", "ðŸ", "ðŸ‘”"],
  "The Matrix": ["ðŸ’Š", "ðŸ”µ", "ðŸ•¶", "ðŸ’»"],
  "Casablanca": ["âœˆ", "â¤ï¸", "â˜•", "ðŸŒƒ"],
  "Raiders of the Lost Ark": ["ðŸ—º", "ðŸº", "ðŸ‘£", "ðŸ”¦", "ðŸœ"],
  "V for Vendetta": ["ðŸŽ­", "ðŸ”¥", "ðŸ’£", "ðŸ—¡"],
  "The Princess Bride": ["ðŸ¤º", "â¤ï¸", "âš”", "ðŸ‘‘", "ðŸ°"],
  "Back to the Future": ["âŒš", "âš¡", "ðŸš—", "â³"],
  "Dune": ["ðŸœ", "ðŸ›", "âš”", "ðŸŒŒ"]
}

PyFunc

Resulting in output: [['ðŸ¦', 'ðŸ‘‘', 'ðŸŒ…', 'ðŸŒ¿'], ['ðŸŽƒ', 'ðŸ‘»', 'ðŸ’€', 'ðŸ•¸', 'ðŸ•·'], ['ðŸ”«', 'ðŸ’¼', 'ðŸ', 'ðŸ‘”'], ['ðŸ’Š', 'ðŸ”µ', 'ðŸ•¶', 'ðŸ’»'], ['âœˆ', 'â¤ï¸', 'â˜•', 'ðŸŒƒ'], ['ðŸ—º', 'ðŸº', 'ðŸ‘£', 'ðŸ”¦', 'ðŸœ'], ['ðŸŽ', 'ðŸ”¥', 'ðŸ’£', 'ðŸ—¡'], ['ðŸ¤º', 'â¤ï¸', 'âš”', 'ðŸ‘‘', 'ðŸ°'], ['âŒš', 'âš¡', 'ðŸš—', 'â³'], ['ðŸœ', 'ðŸ›', 'âš”', 'ðŸŒŒ']]

LLM Generation

Query

For each of the following ten movies described by 5 emoji, give the movie title that best matches. Give your answer as a JSON list. So If I asked for

You might answer

["Finding Nemo", "Toy Story"]]

. Now give me answers for these movies:

['ðŸŽƒ', 'ðŸ‘»', 'ðŸ’€', 'ðŸ•¸', 'ðŸ•·'],
['ðŸ”«', 'ðŸ’¼', 'ðŸ', 'ðŸ‘”'],
['ðŸ’Š', 'ðŸ”µ', 'ðŸ•¶', 'ðŸ’»'],
['âœˆ', 'â¤ï¸', 'â˜•', 'ðŸŒƒ'],
['ðŸ—º', 'ðŸº', 'ðŸ‘£', 'ðŸ”¦', 'ðŸœ'],
['ðŸŽ­', 'ðŸ”¥', 'ðŸ’£', 'ðŸ—¡'],
['ðŸ¤º', 'â¤ï¸', 'âš”', 'ðŸ‘‘', 'ðŸ°'],
['âŒš', 'âš¡', 'ðŸš—', 'â³'],
['ðŸœ', 'ðŸ›', 'âš”', 'ðŸŒŒ']]

What are the names of the movie titles?

Output

[
  "The Lion King",
  "Halloween",
  "The Godfather",
  "The Matrix",
  "Up in the Air",
  "Raiders of the Lost Ark",
  "V for Vendetta",
  "The Princess Bride",
  "Back to the Future",
  "Dune"
]

Extract Json

I extracted the following JSON from that output:

[
  "The Lion King",
  "Halloween",
  "The Godfather",
  "The Matrix",
  "Up in the Air",
  "Raiders of the Lost Ark",
  "V for Vendetta",
  "The Princess Bride",
  "Back to the Future",
  "Dune"
]

PyFunc

OK Resulting in output: True