OpenAI fine-tuning API in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to start fine-tuning a model using the OpenAI API.

Prompt Engineering / GenAI

response = openai.FineTune.create(training_file=[1])

Drag options to blanks, or click blank then click option'

A'file-abc123'

Bopenai.File.create()

C12345

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Passing an integer instead of a string file ID.

Using a file upload function instead of the file ID.

Passing None or empty value.

The training_file parameter requires the file ID string, such as 'file-abc123'.

2fill in blank

medium

Complete the code to list all fine-tune jobs using the OpenAI API.

Prompt Engineering / GenAI

fine_tunes = openai.FineTune.[1]()

Drag options to blanks, or click blank then click option'

Acreate

Blist

Cretrieve

Ddelete

Attempts:

3 left

💡 Hint

Common Mistakes

Using create() instead of list().

Using retrieve() which requires a job ID.

Using delete() which removes a job.

The list() method returns all fine-tune jobs.

3fill in blank

hard

Fix the error in the code to retrieve a fine-tune job by ID.

Prompt Engineering / GenAI

job = openai.FineTune.[1](id='ft-A1B2C3')

Drag options to blanks, or click blank then click option'

Aretrieve

Bcreate

Clist

Dupdate

Attempts:

3 left

💡 Hint

Common Mistakes

Using list() which returns all jobs, not one.

Using create() which starts a new job.

Using update() which modifies a job.

The retrieve() method fetches a fine-tune job by its ID.

4fill in blank

hard

Fill both blanks to create a fine-tune job with a validation file.

Prompt Engineering / GenAI

response = openai.FineTune.create(training_file=[1], [2]=validation_file)

Drag options to blanks, or click blank then click option'

Avalidation_file

B'file-train123'

Cvalidation_file_id

Dvalidation_file_name

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect parameter names like 'validation_file_name'.

Passing the wrong variable or literal for the training file.

The training_file parameter requires the file ID string like 'file-train123'. The parameter name for the validation file is 'validation_file', assuming `validation_file` is a variable holding the validation file ID.

5fill in blank

hard

Fill all three blanks to create a fine-tune job with training file, validation file, and model name.

Prompt Engineering / GenAI

response = openai.FineTune.create(training_file=[1], validation_file=[2], model=[3])

Drag options to blanks, or click blank then click option'

Avalidation_file

Bmodel

C'curie'

D'file-xyz789'

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up parameter names and values.

Using incorrect strings for file IDs or model names.

Forgetting to specify the base model.

The training_file is the file ID 'file-xyz789', the validation_file uses variable 'validation_file' (holding its ID), and model is the string 'curie'.

Practice

1. What is the main purpose of using the OpenAI fine-tuning API?

easy

A. To customize a base AI model with your own training data

B. To create a new AI model from scratch without any data

C. To delete existing AI models permanently

D. To convert AI models into images

Solution

1. Step 1: Understand fine-tuning concept

   Fine-tuning means adjusting a pre-trained AI model using your own data to make it better for your specific task.

2. Step 2: Identify the API's role

   The OpenAI fine-tuning API helps you upload your data and create a customized version of an existing model.

3. Final Answer:

   To customize a base AI model with your own training data -> Option A

4. Quick Check:

   Fine-tuning = Customize model with your data [OK]

Hint: Fine-tuning means customizing existing models with your data [OK]

Common Mistakes:

• Thinking fine-tuning creates models from scratch
• Confusing fine-tuning with deleting models
• Assuming fine-tuning changes model type (like image conversion)

2. Which of the following is the correct way to start a fine-tuning job using the OpenAI API in Python?

easy

A. openai.createFineTune(training='file-abc123')

B. openai.FineTune.create(training_file='file-abc123')

C. openai.fine_tune.start(file='file-abc123')

D. openai.finetune.upload(file='file-abc123')

Solution

1. Step 1: Recall OpenAI fine-tuning syntax

   The official OpenAI Python client uses openai.FineTune.create() to start fine-tuning jobs.

2. Step 2: Check parameter names

   The parameter for training data file is training_file, matching openai.FineTune.create(training_file='file-abc123') exactly.

3. Final Answer:

   openai.FineTune.create(training_file='file-abc123') -> Option B

4. Quick Check:

   Correct method and parameter = openai.FineTune.create(training_file='file-abc123') [OK]

Hint: Use openai.FineTune.create with training_file parameter [OK]

Common Mistakes:

• Using incorrect method names like fine_tune.start
• Wrong parameter names like 'file' instead of 'training_file'
• Mixing upload and create methods

3. Given this Python code snippet using OpenAI API, what will be the output?

response = openai.FineTune.create(training_file='file-xyz789')
print(response['status'])

medium

A. 'pending'

B. 'completed'

C. 'error'

D. 'unknown'

Solution

1. Step 1: Understand fine-tuning job lifecycle

   When a fine-tuning job is created, its initial status is usually 'pending' as it waits to start processing.

2. Step 2: Analyze code output

   The code prints the 'status' field from the response, which will be 'pending' immediately after creation.

3. Final Answer:

   'pending' -> Option A

4. Quick Check:

   New fine-tune job status = 'pending' [OK]

Hint: New fine-tune jobs start with status 'pending' [OK]

Common Mistakes:

• Assuming status is 'completed' right after creation
• Expecting 'error' without any failure
• Confusing status with model name

4. You wrote this code to fine-tune a model but get an error:

openai.FineTune.create(training_file='file-123')

What is the most likely cause of the error?

medium

A. The API key is missing from the code

B. The method name should be 'fine_tune.create' instead

C. You must specify the model parameter in create()

D. The training file ID 'file-123' is invalid or not uploaded

Solution

1. Step 1: Check common fine-tuning errors

   Errors often happen if the training file ID is wrong or the file was not uploaded properly.

2. Step 2: Validate method and parameters

   The method name and parameters are correct; model parameter is optional for fine-tuning base models.

3. Step 3: Consider API key

   Missing API key causes authentication errors, not file ID errors.

4. Final Answer:

   The training file ID 'file-123' is invalid or not uploaded -> Option D

5. Quick Check:

   Invalid file ID causes error [OK]

Hint: Check if training file ID is correct and uploaded [OK]

Common Mistakes:

• Using wrong method name with underscores
• Forgetting to upload training file before fine-tuning
• Assuming model parameter is always required

5. You want to fine-tune a model to improve chatbot responses for customer support. Which steps should you follow using the OpenAI fine-tuning API?

hard

A. Train a model locally without using OpenAI API

B. Directly call chat completions with the base model without uploading data

C. Upload a JSONL training file, create a fine-tune job with it, then use the new model for chat

D. Upload any text file and call openai.ChatCompletion.create without fine-tuning

Solution

1. Step 1: Prepare training data

   Fine-tuning requires a JSONL file with prompt-completion pairs relevant to customer support.

2. Step 2: Use OpenAI API to create fine-tune job

   Upload the file, then call openai.FineTune.create() with the training file ID.

3. Step 3: Use the fine-tuned model

   After training completes, use the new model for chat completions to get improved responses.

4. Final Answer:

   Upload a JSONL training file, create a fine-tune job with it, then use the new model for chat -> Option C

5. Quick Check:

   Fine-tune with data, then use new model [OK]

Hint: Upload data, fine-tune, then use new model for better chat [OK]

Common Mistakes:

• Skipping data upload and fine-tuning steps
• Trying to train models locally without OpenAI API
• Using base model without fine-tuning for custom tasks