Emerging trends (smaller models, edge AI) in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main advantage of smaller AI models compared to larger ones?

Smaller AI models require less computing power and memory, making them faster and easier to run on devices like smartphones or edge devices.

beginner

Define Edge AI in simple terms.

Edge AI means running AI directly on devices like phones or sensors instead of sending data to big computers far away. This helps with faster decisions and better privacy.

intermediate

Why is Edge AI important for real-time applications?

Edge AI processes data locally, so it can respond instantly without waiting for internet communication, which is crucial for things like self-driving cars or health monitors.

intermediate

Name two challenges when using smaller AI models.

Smaller models might have less accuracy and can struggle with very complex tasks compared to bigger models.

intermediate

How do smaller models and Edge AI together benefit users?

Smaller models can run efficiently on edge devices, enabling quick, private, and energy-saving AI services without needing constant internet connection.

What is a key benefit of running AI on edge devices?

A Faster response times

B Requires more internet bandwidth

C Needs bigger models

D Always needs cloud servers

Edge AI processes data locally, so it responds faster without relying on internet speed.

Smaller AI models are best described as:

A Models that use less computing resources

B Models that always have higher accuracy

C Models that need large servers to run

D Models that cannot run on phones

Smaller models are designed to use less memory and computing power.

Which is NOT a typical use case for Edge AI?

A Smart home devices

B Self-driving cars

C Offline language translation

D Large-scale cloud data storage

Large-scale cloud data storage is done in the cloud, not on edge devices.

What is a common challenge when using smaller AI models?

A They always require internet

B They may have lower accuracy

C They use more energy

D They cannot run on edge devices

Smaller models often trade off some accuracy for efficiency.

Edge AI helps improve privacy because:

A It requires constant internet connection

B It sends all data to big servers

C Data is processed locally and not sent to the cloud

D It uses bigger models

Processing data locally means sensitive information stays on the device.

Explain how smaller AI models and Edge AI work together to improve user experience.

Think about speed, privacy, and device limitations.

Describe two challenges faced when deploying AI on edge devices.

Consider device limits and model performance.

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