OpenAI fine-tuning API in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding fine-tuning data format

Which of the following best describes the required format for training data when using the OpenAI fine-tuning API?

A A plain text file with prompts and completions separated by a blank line.

B A CSV file with columns 'input' and 'output' containing raw text data.

C A JSONL file where each line is a JSON object with keys 'prompt' and 'completion', both as strings.

D An XML file with nested tags for prompts and completions.

💡 Hint

Think about the file format that supports line-by-line JSON objects.

The OpenAI fine-tuning API requires training data in JSONL format, where each line is a JSON object with 'prompt' and 'completion' keys as strings.

❓ Model Choice

intermediate

Choosing the base model for fine-tuning

You want to fine-tune a model for a task requiring understanding of complex instructions and generating detailed text. Which base model should you choose from OpenAI's options?

A curie

B ada

C babbage

D davinci

💡 Hint

Consider the model with the highest capability for complex tasks.

The 'davinci' model is the most capable among OpenAI's base models, suitable for complex instructions and detailed text generation.

❓ Hyperparameter

advanced

Effect of 'n_epochs' in fine-tuning

What is the effect of increasing the 'n_epochs' parameter during OpenAI fine-tuning?

A The model trains for more passes over the training data, potentially improving fit but risking overfitting.

B The model uses more layers internally to increase capacity.

C The learning rate is automatically increased to speed up training.

D The batch size is reduced to improve gradient estimation.

💡 Hint

Think about what 'epochs' means in machine learning training.

'n_epochs' controls how many times the model sees the entire training dataset. More epochs can improve learning but may cause overfitting.

❓ Metrics

advanced

Evaluating fine-tuned model performance

After fine-tuning a model with OpenAI's API, which metric is most appropriate to evaluate its performance on a text generation task?

A Perplexity on a held-out validation set

B Accuracy of classification labels

C Mean squared error between input and output

D Confusion matrix of predicted classes

💡 Hint

Consider metrics used for language models generating text.

Perplexity measures how well a language model predicts a sample and is commonly used for text generation evaluation.

🔧 Debug

expert

Diagnosing fine-tuning failure due to data formatting

You submitted a JSONL file for fine-tuning but received an error: 'Invalid JSONL format'. Which of the following issues would cause this error?

A The file uses UTF-8 encoding

B One line in the file is missing a closing brace '}'

C Each line contains a valid JSON object with 'prompt' and 'completion'

D The file has Unix-style line endings '\n'

💡 Hint

Invalid JSON means a syntax problem in the JSON structure.

Missing a closing brace causes invalid JSON syntax, leading to JSONL format errors during fine-tuning.