القائمة
Structured prompt template for recovering the instruction that transformed one text into another, with labeled original/rewritten fields and explicit task framing
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Memory-efficient Keras generator that streams sharded CSV files in chunks, renders strokes to images on-the-fly, and yields batches for training on datasets too large for memory
Stack 1D convolutions for local feature extraction before bidirectional LSTMs to classify variable-length stroke sequences into hundreds of doodle categories
Partition a large dataset into N balanced shards using integer key modulo arithmetic for reproducible, class-interleaved splits across CSV files
Normalize raw stroke coordinates to 0-255 range, resample at uniform arc-length spacing, then apply Ramer-Douglas-Peucker simplification
Render stroke sequences to grayscale images with temporal intensity encoding where earlier strokes are brighter and later strokes fade to encode drawing order
Score each face in a video as anomalous by computing L2 distance from the embedding centroid of all faces, then convert to probability via logistic function
| name | llm-inverse-task-prompt-template |
| description | Structured prompt template for recovering the instruction that transformed one text into another, with labeled original/rewritten fields and explicit task framing |
To recover an unknown prompt from an (original, rewritten) text pair, frame the task explicitly: show the LLM both texts with clear labels, state that one was derived from the other via a prompt, and ask it to infer that prompt. The template structure — labeled fields, task description, output format constraint — is critical for consistent results across different LLMs.
TEMPLATE = """Below, the `Original Text` has been rewritten into `Rewritten Text`
by an LLM with a certain prompt/instruction. Analyze the differences
and infer the specific prompt that was used.
Original Text:
{original_text}
Rewritten Text:
{rewritten_text}
The prompt was:"""
def build_recovery_prompt(original, rewritten):
return TEMPLATE.format(
original_text=original[:1000],
rewritten_text=rewritten[:1000])
prompt = build_recovery_prompt(row['original_text'], row['rewritten_text'])
output = model.generate(tokenizer(prompt, return_tensors='pt').to(device),
max_new_tokens=50, do_sample=False)
recovered = tokenizer.decode(output[0], skip_special_tokens=True)
recovered = recovered.split("The prompt was:")[-1].strip()
Original Text:, Rewritten Text:)The prompt was:) to constrain output formatThe prompt was: forces concise output without preamble