Short-term memory (conversation context) in Agentic AI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Short-term Memory in Conversation Context

Which of the following best describes the role of short-term memory in conversation context for an AI agent?

A It temporarily holds recent conversation details to maintain context during interaction.

B It stores all past conversations permanently for future reference.

C It deletes all conversation data immediately after each user input.

D It only stores user preferences without any conversation details.

💡 Hint

Think about how an AI keeps track of what was just said to respond properly.

Short-term memory in conversation context temporarily holds recent information to help the AI understand and respond appropriately during an ongoing interaction.

❓ Model Choice

intermediate

Choosing a Model for Short-term Memory in Dialogue

Which model architecture is best suited for capturing short-term memory in conversation context?

A K-Nearest Neighbors (KNN) for classification

B Recurrent Neural Network (RNN) with gated units like LSTM or GRU

C Convolutional Neural Network (CNN) for image recognition

D Feedforward Neural Network without recurrence

💡 Hint

Consider models designed to handle sequences and remember recent inputs.

RNNs with gated units like LSTM or GRU are designed to remember recent inputs, making them suitable for short-term memory in conversations.

❓ Metrics

advanced

Evaluating Short-term Memory Effectiveness

Which metric would best help evaluate how well an AI model retains short-term conversation context?

A BLEU score measuring translation quality

B Perplexity measuring prediction uncertainty on next tokens

C Recall measuring how many relevant recent context tokens are used in response

D Mean Squared Error measuring regression accuracy

💡 Hint

Think about measuring how much recent relevant information is captured in the output.

Recall measures how many relevant recent context tokens are included in the AI's response, indicating how well short-term memory is used.

🔧 Debug

advanced

Debugging Short-term Memory Loss in Conversation

An AI agent forgets the last user question immediately after responding. Which of the following is the most likely cause?

A The AI is using a large context window.

B The training data is too large.

C The model uses attention mechanisms correctly.

D The short-term memory buffer is cleared after each response.

💡 Hint

Consider what happens if the memory holding recent conversation is reset too soon.

If the short-term memory buffer is cleared after each response, the AI loses recent context and forgets the last user question.

❓ Predict Output

expert

Output of Short-term Memory Simulation Code

What is the output of this Python code simulating short-term memory with a fixed-size queue?

Agentic AI

from collections import deque

memory = deque(maxlen=3)
inputs = ['Hi', 'How are you?', 'What is AI?', 'Tell me a joke']
outputs = []
for inp in inputs:
    memory.append(inp)
    outputs.append(list(memory))
print(outputs)

A [['Hi'], ['Hi', 'How are you?'], ['Hi', 'How are you?', 'What is AI?'], ['How are you?', 'What is AI?', 'Tell me a joke']]

B [['Hi'], ['How are you?'], ['What is AI?'], ['Tell me a joke']]

C [['Hi'], ['Hi', 'How are you?'], ['Hi', 'How are you?', 'What is AI?'], ['Hi', 'How are you?', 'What is AI?', 'Tell me a joke']]

D [['Hi'], ['Hi', 'How are you?'], ['How are you?', 'What is AI?'], ['What is AI?', 'Tell me a joke']]

💡 Hint

Remember that deque with maxlen drops oldest items when full.

The deque holds up to 3 items. After adding the fourth input, the oldest ('Hi') is removed, so the last list contains the last three inputs.

Practice

1. What is the main purpose of short-term memory in an AI conversation?

easy

A. To remember recent messages and keep the conversation connected

B. To store all past conversations permanently

C. To delete irrelevant messages immediately

D. To speed up the AI's processing by ignoring context

Solution

1. Step 1: Understand short-term memory role

   Short-term memory stores recent conversation parts to keep context.

2. Step 2: Compare options with this role

   Only To remember recent messages and keep the conversation connected matches this purpose; others describe different or incorrect functions.

3. Final Answer:

   To remember recent messages and keep the conversation connected -> Option A

4. Quick Check:

   Short-term memory = recent context [OK]

Hint: Short-term memory = recent messages stored [OK]

Common Mistakes:

• Confusing short-term with long-term memory
• Thinking it stores all past conversations
• Believing it deletes messages immediately

2. Which of the following is the correct way to represent short-term memory storing the last 3 messages in Python?

easy

A. short_term_memory = messages[0]

B. short_term_memory = messages[:3]

C. short_term_memory = messages[3:]

D. short_term_memory = messages[-3:]

Solution

1. Step 1: Understand Python list slicing for last 3 items

   Using messages[-3:] gets the last 3 messages from the list.

2. Step 2: Check other options

   messages[:3] gets first 3, messages[3:] gets from 4th to end, messages[0] gets only first message.

3. Final Answer:

   short_term_memory = messages[-3:] -> Option D

4. Quick Check:

   Last 3 messages slice = messages[-3:] [OK]

Hint: Negative slice gets last items in list [OK]

Common Mistakes:

• Using positive slice for last items
• Selecting only one message instead of three
• Confusing start and end indices

3. Given the code below, what will be the output of print(short_term_memory)?

messages = ['Hi', 'How are you?', 'I am fine', 'What about you?', 'Good!']
short_term_memory = messages[-2:]
print(short_term_memory)

medium

A. ['Hi', 'How are you?']

B. ['I am fine', 'What about you?']

C. ['What about you?', 'Good!']

D. ['Good!']

Solution

1. Step 1: Understand list slicing with negative indices

   messages[-2:] selects the last two items from the list.

2. Step 2: Identify last two messages

   The last two messages are 'What about you?' and 'Good!'.

3. Final Answer:

   ['What about you?', 'Good!'] -> Option C

4. Quick Check:

   messages[-2:] = last two messages [OK]

Hint: Negative slice picks last elements [OK]

Common Mistakes:

• Selecting wrong slice range
• Confusing order of messages
• Printing only one message instead of two

4. The following code is intended to keep only the last 3 messages in short-term memory, but it has a bug. What is the bug?

messages = ['Hello', 'What is AI?', 'Tell me more', 'Thanks']
short_term_memory = messages[3:]
print(short_term_memory)

medium

A. It causes an IndexError

B. It keeps only the last message instead of last three

C. It keeps the first three messages instead of last three

D. It clears the list completely

Solution

1. Step 1: Analyze the slice messages[3:]

   This slice starts at index 3 and goes to the end, so it keeps only the last message 'Thanks'.

2. Step 2: Compare with intended behavior

   The goal was to keep last 3 messages, but this code keeps only one message.

3. Final Answer:

   It keeps only the last message instead of last three -> Option B

4. Quick Check:

   messages[3:] = last message only [OK]

Hint: Check slice start index carefully [OK]

Common Mistakes:

• Assuming slice keeps last 3 messages
• Expecting an error when none occurs
• Confusing slice start and end

5. You want an AI agent to remember the last 4 messages in a conversation to keep context. The conversation messages are stored in a list called chat_history. Which code snippet correctly updates the short-term memory to always hold the last 4 messages after adding a new message new_msg?

hard

A. chat_history.append(new_msg)
short_term_memory = chat_history[-4:]

B. short_term_memory = chat_history[:4]
chat_history.append(new_msg)

C. short_term_memory = chat_history[-4:]
chat_history.append(new_msg)

D. chat_history = chat_history[-4:]
short_term_memory = new_msg

Solution

1. Step 1: Add new message to chat_history first

   Appending new_msg to chat_history updates the conversation.

2. Step 2: Slice last 4 messages for short-term memory

   Using chat_history[-4:] gets the last 4 messages including the new one.

3. Final Answer:

   chat_history.append(new_msg) short_term_memory = chat_history[-4:] -> Option A

4. Quick Check:

   Append then slice last 4 messages [OK]

Hint: Append first, then slice last 4 [OK]

Common Mistakes:

• Slicing before appending new message
• Assigning new message alone as memory
• Slicing first 4 messages instead of last 4