Emerging trends (smaller models, edge AI) in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a smaller AI model for edge devices.

Prompt Engineering / GenAI

model = SmallModel(input_size=128, output_size=10, [1]=2)

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

Alayers

Bepochs

Cbatch_size

Dlearning_rate

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing training parameters like epochs or batch size with model architecture.

The 'layers' parameter defines how many layers the smaller model has, which is key for edge AI.

2fill in blank

medium

Complete the code to quantize the model for efficient edge deployment.

Prompt Engineering / GenAI

quantized_model = quantize(model, [1]='int8')

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

Aprecision

Bformat

Ctype

Dmode

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'format' or 'type' which are not the correct parameter names here.

The 'precision' parameter specifies the number format, such as int8, to reduce model size.

3fill in blank

hard

Fix the error in the code to deploy the model on an edge device.

Prompt Engineering / GenAI

device = get_device('[1]')
model.to(device)

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

Agpu

Bcpu

Ctpu

Dedge

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'gpu' or 'tpu' which are not commonly available on edge devices.

Edge devices often use CPUs, so 'cpu' is the correct device string for deployment.

4fill in blank

hard

Fill both blanks to create a dictionary of model sizes and their latency on edge devices.

Prompt Engineering / GenAI

results = { [1]: [2] for [1], [2] in zip(sizes, latencies) }

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

Asize

Blatency

Csizes

Dlatencies

Attempts:

3 left

💡 Hint

Common Mistakes

Using plural variable names as keys or values, which causes errors.

The dictionary keys and values should be the singular 'size' and 'latency' from the zipped lists.

5fill in blank

hard

Fill all three blanks to filter models smaller than 10MB and with latency under 50ms.

Prompt Engineering / GenAI

filtered = { [1]: [2] for [1], [2] in models.items() if [3] < 10 and [2] < 50 }

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

Amodel_name

Bsize

Dlatency

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up keys and values or using wrong variable names in the condition.

Keys are size, values are latency, and size is used in the condition to filter models.

Practice

1. What is a key benefit of smaller AI models in devices like smartphones?

easy

A. They need large servers to work

B. They require constant internet connection

C. They run faster and use less memory

D. They always produce more accurate results

Solution

1. Step 1: Understand smaller AI models

   Smaller AI models are designed to be lightweight, so they use less memory and compute power.

2. Step 2: Identify benefits for smartphones

   Because phones have limited memory and processing power, smaller models help them run AI tasks faster and more efficiently.

3. Final Answer:

   They run faster and use less memory -> Option C

4. Quick Check:

   Smaller models = faster, less memory [OK]

Hint: Smaller models save memory and speed up devices [OK]

Common Mistakes:

• Thinking smaller models need big servers
• Assuming smaller models require internet
• Believing smaller models always improve accuracy

2. Which code snippet correctly shows a simple way to run AI on an edge device?

easy

A. model = download_model('cloud_model')
result = model.predict_online(input_data)

B. model = load_model('big_model.h5')
result = model.train(input_data)

C. model = load_model('small_model.tflite')
result = model.upload(input_data)

D. model = load_model('small_model.tflite')
result = model.predict(input_data)

Solution

1. Step 1: Identify edge AI code

   Edge AI runs AI models locally, so loading a small model like 'small_model.tflite' fits this.

2. Step 2: Check correct method usage

   Using predict runs inference, which is typical for edge AI. Training or uploading is not common on edge devices.

3. Final Answer:

   model = load_model('small_model.tflite') result = model.predict(input_data) -> Option D

4. Quick Check:

   Load small model + predict locally = edge AI [OK]

Hint: Edge AI loads small models and predicts locally [OK]

Common Mistakes:

• Using training instead of prediction on edge
• Downloading models from cloud during inference
• Uploading data instead of predicting locally

3. Given this Python code simulating edge AI inference, what is the printed output?

class SmallModel:
    def predict(self, x):
        return x * 2

model = SmallModel()
result = model.predict(5)
print(result)

medium

A. 10

B. 5

C. 25

D. Error

Solution

1. Step 1: Understand the predict method

   The method multiplies input x by 2 and returns it.

2. Step 2: Calculate the output for input 5

   5 * 2 = 10, so result is 10.

3. Final Answer:

   10 -> Option A

4. Quick Check:

   5 times 2 equals 10 [OK]

Hint: Multiply input by 2 as per predict method [OK]

Common Mistakes:

• Confusing multiplication with addition
• Expecting input as output
• Assuming code causes error

4. This code tries to run AI on an edge device but has an error. What is the problem?

input_data = [1, 2, 3]
model = load_model('small_model.tflite')
result = model.train(input_data)
print(result)

medium

A. The model file name is incorrect

B. Edge devices usually do not train models, only predict

C. The print statement is missing parentheses

D. The input_data variable is not defined

Solution

1. Step 1: Understand edge AI capabilities

   Edge AI devices typically run inference (predict), not training, because training needs more resources.

2. Step 2: Identify incorrect method usage

   Calling train on the model is incorrect for edge AI; it should be predict.

3. Final Answer:

   Edge devices usually do not train models, only predict -> Option B

4. Quick Check:

   Edge AI = predict, not train [OK]

Hint: Edge AI predicts, does not train models [OK]

Common Mistakes:

• Thinking edge devices can train models
• Assuming file name causes error
• Ignoring method misuse

5. You want to build a voice assistant that works offline on a smartwatch. Which approach best fits this edge AI trend?

hard

A. Use a small AI model running locally on the watch

B. Use no AI and only pre-recorded responses

C. Stream audio to a server for processing

D. Use a large cloud AI model accessed via internet

Solution

1. Step 1: Understand offline edge AI needs

   Offline means no internet, so AI must run locally on the device.

2. Step 2: Choose model size for smartwatch

   Smartwatches have limited memory and power, so a small AI model is best to run locally.

3. Final Answer:

   Use a small AI model running locally on the watch -> Option A

4. Quick Check:

   Offline + smartwatch = small local model [OK]

Hint: Offline device needs small local AI model [OK]

Common Mistakes:

• Choosing cloud models needing internet
• Streaming audio defeats offline goal
• Ignoring device memory limits