Why Model selection (GPT-4, GPT-3.5) in Prompt Engineering / GenAI? - Purpose & Use Cases

The Big Idea

What if choosing the wrong AI model is silently costing you time and money every day?

The Scenario

Imagine you have two powerful AI helpers, GPT-4 and GPT-3.5, but you don't know which one to pick for your task. You try guessing which model will give better answers for your project without testing. It's like choosing a tool blindly without knowing if it fits the job.

The Problem

Picking a model without a clear plan wastes time and money. You might use the bigger GPT-4 for simple tasks, which is slow and costly. Or use GPT-3.5 for complex needs and get poor results. This trial-and-error is frustrating and can lead to wrong decisions.

The Solution

Model selection helps you choose the right AI model by comparing their strengths and weaknesses for your specific task. It saves effort by testing and measuring which model works best, so you get faster, cheaper, and more accurate results.

Before vs After

✗ Before

response = gpt4.generate(text)
# or just guess which model to use

✓ After

if task_complexity > threshold:
    response = gpt4.generate(text)
else:
    response = gpt3_5.generate(text)

What It Enables

It lets you get the best AI help for your needs without wasting resources or time.

Real Life Example

A company uses GPT-3.5 for quick customer replies but switches to GPT-4 for detailed reports, balancing speed and quality perfectly.

Key Takeaways

Manual guessing wastes time and money.

Model selection finds the best AI for your task.

This leads to smarter, faster, and cheaper AI use.

Practice

1. Which model should you choose if you need detailed and complex text generation?

easy

A. GPT-3.5

B. Both are equally detailed

C. GPT-4

D. Neither, use a smaller model

Solution

1. Step 1: Understand model capabilities

   GPT-4 is designed for more complex and detailed tasks compared to GPT-3.5.

2. Step 2: Match task complexity to model

   For detailed and complex text generation, GPT-4 is the better choice.

3. Final Answer:

   GPT-4 -> Option C

4. Quick Check:

   Complex tasks = GPT-4 [OK]

Hint: Choose GPT-4 for complexity, GPT-3.5 for speed [OK]

Common Mistakes:

• Choosing GPT-3.5 for complex tasks
• Thinking both models have same detail level

2. Which of the following is the correct way to specify GPT-3.5 in an API call?

easy

A. "model": "gpt-3.5-turbo"

B. "model": "gpt-3"

C. "model": "gpt-4"

D. "model": "gpt-5"

Solution

1. Step 1: Recall model naming conventions

   The GPT-3.5 model is named "gpt-3.5-turbo" in API calls.

2. Step 2: Identify correct option

   "model": "gpt-3.5-turbo" matches the exact model name for GPT-3.5.

3. Final Answer:

   "model": "gpt-3.5-turbo" -> Option A

4. Quick Check:

   Correct model name = "model": "gpt-3.5-turbo" [OK]

Hint: Use exact model name string in API call [OK]

Common Mistakes:

• Using "gpt-3" instead of "gpt-3.5-turbo"
• Confusing GPT-4 name with GPT-3.5

3. Given this code snippet calling the OpenAI API, which model will produce faster responses but possibly less detailed output?

response = openai.ChatCompletion.create(
  model="gpt-3.5-turbo",
  messages=[{"role": "user", "content": "Explain photosynthesis."}]
)

medium

A. GPT-3.5, faster but less detailed

B. GPT-4, slower but more detailed

C. GPT-4, faster and more detailed

D. GPT-3.5, slower but more detailed

Solution

1. Step 1: Identify the model used in code

   The code uses "gpt-3.5-turbo" as the model parameter.

2. Step 2: Recall model speed and detail tradeoff

   GPT-3.5 is faster but less detailed compared to GPT-4.

3. Final Answer:

   GPT-3.5, faster but less detailed -> Option A

4. Quick Check:

   Model in code = GPT-3.5 [OK]

Hint: Check model name string to know speed/detail tradeoff [OK]

Common Mistakes:

• Assuming GPT-3.5 is slower
• Confusing model names in code snippet

4. You wrote this API call but get an error:

response = openai.ChatCompletion.create(
  model="gpt-3.5",
  messages=[{"role": "user", "content": "Tell me a joke."}]
)

What is the likely problem?

medium

A. Messages list is missing a system role

B. Model name is incomplete, should be "gpt-3.5-turbo"

C. API key is missing

D. The model "gpt-3.5" does not exist

Solution

1. Step 1: Check model name correctness

   The model name "gpt-3.5" is incomplete; the correct full name is "gpt-3.5-turbo".

2. Step 2: Understand error cause

   Using an incomplete model name causes the API to reject the call.

3. Final Answer:

   Model name is incomplete, should be "gpt-3.5-turbo" -> Option B

4. Quick Check:

   Model name must be exact [OK]

Hint: Use full model name string to avoid errors [OK]

Common Mistakes:

• Using partial model names
• Assuming system role is mandatory
• Ignoring API key errors

5. You want to build a chatbot that answers customer questions quickly and cheaply but can switch to detailed answers when needed. How should you select models in your code?

hard

A. Always use GPT-4 for all answers

B. Use GPT-4 only, it is always more accurate

C. Use GPT-3.5 only, it is always faster and cheaper

D. Use GPT-3.5 for quick replies, switch to GPT-4 for detailed ones

Solution

1. Step 1: Understand tradeoffs between GPT-3.5 and GPT-4

   GPT-3.5 is faster and cheaper but less detailed; GPT-4 is slower and costlier but more detailed.

2. Step 2: Match chatbot needs to model selection

   Use GPT-3.5 for quick, cheap answers and switch to GPT-4 when detailed responses are needed.

3. Final Answer:

   Use GPT-3.5 for quick replies, switch to GPT-4 for detailed ones -> Option D

4. Quick Check:

   Balance speed and detail with model switching [OK]

Hint: Switch models based on answer detail needed [OK]

Common Mistakes:

• Using only one model for all tasks
• Ignoring cost and speed tradeoffs