Model selection (GPT-4, GPT-3.5) in Prompt Engineering / GenAI - Model Pipeline Trace

Model Pipeline - Model selection (GPT-4, GPT-3.5)

This pipeline compares two language models, GPT-4 and GPT-3.5, to select the best one for a text generation task. It processes input prompts, runs them through both models, evaluates outputs, and chooses the model with better performance.

Data Flow - 5 Stages

1Input prompt

1000 prompts x 1 text column→Receive user text prompts for generation→1000 prompts x 1 text column

"Explain photosynthesis in simple terms."

↓

2Preprocessing

1000 prompts x 1 text column→Tokenize text into model-readable tokens→1000 prompts x variable token length

"Explain photosynthesis" -> [101, 2345, 6789, ...]

↓

3Model inference GPT-3.5

1000 prompts x variable token length→Generate text outputs using GPT-3.5→1000 prompts x generated text

"Photosynthesis is the process plants use to make food."

↓

4Model inference GPT-4

1000 prompts x variable token length→Generate text outputs using GPT-4→1000 prompts x generated text

"Photosynthesis allows plants to convert sunlight into energy."

↓

5Evaluation

1000 prompts x 2 generated texts→Score outputs by relevance, correctness, and fluency→1000 prompts x 1 best model label

"GPT-4" chosen for prompt 1, "GPT-3.5" for prompt 2

Training Trace - Epoch by Epoch

Loss
0.5 |****
0.4 |******
0.3 |********
0.2 |**********
0.1 |************
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.45	0.7	Initial training with moderate loss and accuracy
2	0.35	0.78	Loss decreased, accuracy improved
3	0.28	0.83	Model converging well with steady improvement
4	0.22	0.87	Further loss reduction and accuracy gain
5	0.18	0.9	Training nearing optimal performance

Prediction Trace - 4 Layers

Layer 1: Tokenization

Layer 2: GPT-3.5 inference

Layer 3: GPT-4 inference

Layer 4: Evaluation

Model Quiz - 3 Questions

Which model showed better accuracy during training?

A Both equal

B GPT-3.5

C GPT-4

D Not enough information

Key Insight

Model selection compares outputs from different models on the same input to pick the best one. Training metrics like loss and accuracy help understand model quality, while evaluation of generated text ensures the chosen model meets task needs.

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