Chat completions endpoint in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding the role of system messages in chat completions

In the chat completions endpoint, what is the primary purpose of the system message?

A To specify the output format of the AI's response

B To represent the user's input or question to the AI

C To store the AI's previous responses for reference

D To provide instructions or context that guide the AI's behavior throughout the conversation

💡 Hint

Think about which message sets the tone or rules for the AI's replies.

The system message is used to give the AI instructions or context that influence how it responds to user messages. It sets the behavior for the entire chat session.

❓ Predict Output

intermediate

Output of a chat completion request with multiple messages

Given the following chat messages sent to the chat completions endpoint, what will be the role and content of the AI's first response message?

Prompt Engineering / GenAI

messages = [
  {"role": "system", "content": "You are a helpful assistant."},
  {"role": "user", "content": "What is 2 + 2?"}
]

# The AI responds based on these messages.

A {"role": "assistant", "content": "2 + 2 equals 4."}

B {"role": "user", "content": "2 + 2 equals 4."}

C {"role": "system", "content": "2 + 2 equals 4."}

D {"role": "assistant", "content": "I don't understand the question."}

💡 Hint

The AI replies as the assistant role with an answer to the user's question.

The AI always responds with the role 'assistant'. Given the user asked 'What is 2 + 2?', the AI should answer correctly with '2 + 2 equals 4.'

❓ Model Choice

advanced

Choosing the best model for a chat completion task

You want to build a customer support chatbot that can handle complex questions and maintain context over multiple turns. Which model is best suited for this task?

A A large chat-optimized model trained for multi-turn conversations

B A small, fast model optimized for single-turn question answering

C A model specialized in image recognition

D A model designed only for text summarization

💡 Hint

Consider which model type is designed for conversations with context.

Large chat-optimized models are trained to understand and maintain context across multiple messages, making them ideal for chatbots handling complex dialogs.

❓ Hyperparameter

advanced

Effect of temperature parameter in chat completions

What is the effect of increasing the temperature parameter in a chat completions request?

A The AI's responses become shorter and more concise

B The AI's responses become more random and creative

C The AI ignores the system message instructions

D The AI always repeats the same response

💡 Hint

Think about how randomness affects AI text generation.

Higher temperature values increase randomness, making the AI's responses more varied and creative. Lower values make responses more focused and deterministic.

❓ Metrics

expert

Evaluating chat completion quality with perplexity

You have two chat completion models. Model A has a perplexity of 15 on your test set, and Model B has a perplexity of 30. What does this tell you about their performance?

A Both models perform equally well

B Model B predicts the test data better and is more confident in its responses

C Model A predicts the test data better and is more confident in its responses

D Perplexity measures response length, so Model B produces longer answers

💡 Hint

Lower perplexity means better prediction of the data.

Perplexity measures how well a model predicts a sample. Lower perplexity means the model is less 'surprised' by the data and generally performs better.

Practice

1. What is the main purpose of the chat completions endpoint in GenAI?

easy

A. To send messages and receive AI-generated replies in a conversation format

B. To train a new AI model from scratch

C. To upload datasets for AI training

D. To visualize AI model architecture

Solution

1. Step 1: Understand the endpoint's function

   The chat completions endpoint is designed to handle conversations by sending messages and getting AI replies.

2. Step 2: Compare options with the endpoint's purpose

   Only To send messages and receive AI-generated replies in a conversation format describes sending messages and receiving replies, which matches the chat completions endpoint.

3. Final Answer:

   To send messages and receive AI-generated replies in a conversation format -> Option A

4. Quick Check:

   Chat completions endpoint = conversation replies [OK]

Hint: Chat completions = chat messages in, AI replies out [OK]

Common Mistakes:

• Confusing chat completions with model training
• Thinking it uploads data instead of chatting
• Assuming it visualizes model details

2. Which of the following is the correct way to format messages sent to the chat completions endpoint?

easy

A. [{"content": "Hello!"}, {"content": "Hi! How can I help?"}]

B. ["Hello!", "Hi! How can I help?"]

C. {"user": "Hello!", "assistant": "Hi! How can I help?"}

D. [{"role": "user", "content": "Hello!"}, {"role": "assistant", "content": "Hi! How can I help?"}]

Solution

1. Step 1: Recall message format requirements

   The chat completions endpoint expects a list of messages, each with a role and content.

2. Step 2: Match options to the required format

   [{"role": "user", "content": "Hello!"}, {"role": "assistant", "content": "Hi! How can I help?"}] correctly uses a list of dictionaries with "role" and "content" keys, matching the expected format.

3. Final Answer:

   [{"role": "user", "content": "Hello!"}, {"role": "assistant", "content": "Hi! How can I help?"}] -> Option D

4. Quick Check:

   Messages need role and content keys [OK]

Hint: Messages need both role and content keys [OK]

Common Mistakes:

• Sending messages as plain strings without roles
• Using incorrect JSON object structure
• Omitting the role field in messages

3. Given this code snippet using the chat completions endpoint, what will be the output's role and content?

messages = [{"role": "user", "content": "What's the weather?"}]
response = chat_completions(messages=messages, temperature=0.5)
print(response.choices[0].message)

medium

A. {"role": "system", "content": "Weather info not available."}

B. {"role": "user", "content": "What's the weather?"}

C. {"role": "assistant", "content": "I don't have weather data."}

D. An error because temperature is invalid

Solution

1. Step 1: Understand the response structure

   The chat completions endpoint returns a response with choices, each containing a message with role and content.

2. Step 2: Identify the role of the returned message

   The returned message role is "assistant" because the AI replies to the user message.

3. Final Answer:

   {"role": "assistant", "content": "I don't have weather data."} -> Option C

4. Quick Check:

   Response role = assistant, content = AI reply [OK]

Hint: AI replies have role 'assistant' in response [OK]

Common Mistakes:

• Confusing user message with AI reply
• Expecting system role in output
• Thinking temperature causes error here

4. You wrote this code but get an error:

messages = [{"content": "Hello!"}]
response = chat_completions(messages=messages)
print(response.choices[0].message)

What is the likely cause of the error?

medium

A. The messages list should be a string, not a list

B. Missing the 'role' key in the message dictionary

C. The chat_completions function requires a 'temperature' argument

D. The print statement syntax is incorrect

Solution

1. Step 1: Check message format requirements

   Each message must have both 'role' and 'content' keys to be valid.

2. Step 2: Identify missing key in the code

   The message dictionary only has 'content' but lacks the required 'role' key, causing the error.

3. Final Answer:

   Missing the 'role' key in the message dictionary -> Option B

4. Quick Check:

   Every message needs role and content keys [OK]

Hint: Always include 'role' in each message dictionary [OK]

Common Mistakes:

• Assuming temperature is mandatory
• Thinking messages should be a string
• Blaming print statement syntax

5. You want the AI to give more creative and varied answers using the chat completions endpoint. Which parameter should you adjust and how?

hard

A. Increase the temperature value closer to 1 to make responses more creative

B. Decrease the max_tokens to limit response length

C. Set temperature to 0 to get random answers

D. Remove the messages parameter to let AI decide context

Solution

1. Step 1: Understand the role of temperature

   The temperature parameter controls randomness; higher values produce more creative and varied outputs.

2. Step 2: Choose the correct adjustment for creativity

   Increasing temperature closer to 1 encourages creativity, while 0 makes responses deterministic.

3. Final Answer:

   Increase the temperature value closer to 1 to make responses more creative -> Option A

4. Quick Check:

   Higher temperature = more creative answers [OK]

Hint: Higher temperature means more creative AI replies [OK]

Common Mistakes:

• Setting temperature to 0 expecting creativity
• Confusing max_tokens with creativity control
• Removing messages causes loss of context