OpenAI fine-tuning API in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of the OpenAI fine-tuning API?

The OpenAI fine-tuning API allows users to customize a base language model by training it on their own specific data, improving performance on tasks relevant to their needs.

beginner

What type of data format is required for fine-tuning with the OpenAI API?

The data must be in JSONL format, where each line is a JSON object with at least two keys: "prompt" and "completion", representing input and desired output respectively.

intermediate

Which OpenAI model is commonly used as a base for fine-tuning?

Models like "davinci" or "curie" are commonly used as base models for fine-tuning because they provide strong general language understanding.

intermediate

What are the main steps to fine-tune a model using the OpenAI fine-tuning API?

1. Prepare and format your training data in JSONL.
2. Upload the data file to OpenAI.
3. Create a fine-tuning job specifying the base model and training file.
4. Monitor the training process.
5. Use the fine-tuned model for predictions.

intermediate

How can you evaluate the performance of a fine-tuned OpenAI model?

You can evaluate it by testing the model on a set of prompts and comparing the outputs to expected completions, measuring metrics like accuracy, relevance, or loss depending on the task.

What file format must training data be in for OpenAI fine-tuning?

A TXT

B CSV

C XML

D JSONL

OpenAI fine-tuning requires data in JSONL format, where each line is a JSON object with prompt and completion.

Which key is NOT required in each JSONL training example?

A "prompt"

B "label"

C "completion"

D Both A and B are required

Only "prompt" and "completion" keys are required; "label" is not used in OpenAI fine-tuning data.

What is the first step in the OpenAI fine-tuning process?

A Prepare training data

B Create fine-tuning job

C Upload training data

D Use the fine-tuned model

You must first prepare and format your training data before uploading or starting fine-tuning.

Which OpenAI model is commonly used as a base for fine-tuning?

A GPT-2

B ResNet

C davinci

D BERT

"davinci" is a powerful OpenAI model often used as a base for fine-tuning.

How do you monitor the progress of a fine-tuning job?

A Check training logs via API or dashboard

B Wait silently until completion

C Use a separate monitoring tool unrelated to OpenAI

D Fine-tuning jobs do not provide progress info

OpenAI provides logs and status updates via API or dashboard to monitor fine-tuning progress.

Explain the process of fine-tuning a model using the OpenAI fine-tuning API.

Think about the steps from data preparation to using the model.

Describe how you would evaluate if your fine-tuned OpenAI model is working well.

Consider how to check if the model learned what you wanted.

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