LLM wrappers in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is an LLM wrapper?

An LLM wrapper is a simple tool or code that helps you use a large language model (LLM) easily. It hides complex details and lets you talk to the model like a friend.

beginner

Why do we use LLM wrappers?

We use LLM wrappers to make working with big language models easier, faster, and less confusing. They help with sending questions, getting answers, and managing conversations.

intermediate

Name two common features of LLM wrappers.

1. Simplifying input and output handling.
2. Managing conversation history or context automatically.

intermediate

How does an LLM wrapper help with conversation context?

It keeps track of what was said before, so the model can give answers that make sense in the flow of the chat, just like remembering past messages in a conversation.

beginner

Give a simple example of how an LLM wrapper might be used in code.

You write a few lines to send a question to the model and get an answer without handling all the technical details, like this:

response = llm_wrapper.ask('What is AI?')
print(response)

What is the main purpose of an LLM wrapper?

A To simplify interaction with large language models

B To train new language models from scratch

C To store large datasets for training

D To replace human conversation completely

LLM wrappers make it easier to use large language models by hiding complex details.

Which feature is commonly handled by LLM wrappers?

A Creating new neural network layers

B Designing user interfaces

C Optimizing hardware performance

D Managing conversation history

LLM wrappers often keep track of conversation context to improve responses.

An LLM wrapper helps you by:

A Deleting your data automatically

B Making model use more complex

C Simplifying sending questions and getting answers

D Replacing the need for any programming

LLM wrappers simplify the process of interacting with language models.

Which of these is NOT a typical role of an LLM wrapper?

A Training the language model

B Providing easy-to-use functions

C Managing conversation context

D Handling input and output formatting

Training the model is done separately; wrappers focus on usage.

If you want to keep a chat going with an LLM, what does the wrapper help with?

A Increasing the model's size

B Remembering previous messages

C Changing the model's architecture

D Deleting the conversation history

Wrappers help keep track of past messages to maintain context.

Explain in your own words what an LLM wrapper is and why it is useful.

Think about how a wrapper makes talking to a big model easier.

Describe two features that an LLM wrapper provides to improve user experience.

Consider what makes chatting with a model smooth and simple.

Practice

1. What is the main purpose of an LLM wrapper in working with language models?

easy

A. To replace the language model with a simpler algorithm

B. To train the language model from scratch

C. To add extra features like logging and formatting around the model

D. To store large datasets for training

Solution

1. Step 1: Understand what an LLM wrapper does

   An LLM wrapper is a tool that surrounds a language model to add helpful features without changing the model itself.

2. Step 2: Identify the main use of wrappers

   Wrappers add things like logging, formatting, or connecting to other systems to make the model easier to use.

3. Final Answer:

   To add extra features like logging and formatting around the model -> Option C

4. Quick Check:

   LLM wrapper purpose = add features [OK]

Hint: Wrappers add helpers around models, not replace or train them [OK]

Common Mistakes:

• Thinking wrappers train the model
• Confusing wrappers with data storage
• Believing wrappers replace the model

2. Which of the following is the correct way to create a simple LLM wrapper function in Python that adds logging before calling the model's generate method?

easy

A. def wrapper(model, prompt): return print('Calling model', model.generate(prompt))

B. def wrapper(model, prompt): model.generate(prompt); print('Calling model')

C. def wrapper(model, prompt): print('Calling model') model.generate(prompt)

D. def wrapper(model, prompt): print('Calling model'); return model.generate(prompt)

Solution

1. Step 1: Check function syntax and order

   The function should print a message before calling model.generate(prompt) and return the result.

2. Step 2: Identify correct syntax and return usage

   def wrapper(model, prompt): print('Calling model'); return model.generate(prompt) prints first, then returns the model output correctly. def wrapper(model, prompt): model.generate(prompt); print('Calling model') prints after calling but does not return the output properly. def wrapper(model, prompt): return print('Calling model', model.generate(prompt)) returns the print result (None). def wrapper(model, prompt): print('Calling model') model.generate(prompt) misses a semicolon or newline between statements.

3. Final Answer:

   def wrapper(model, prompt): print('Calling model'); return model.generate(prompt) -> Option D

4. Quick Check:

   Print then return output = def wrapper(model, prompt): print('Calling model'); return model.generate(prompt) [OK]

Hint: Print before return, and return model output directly [OK]

Common Mistakes:

• Returning print() instead of model output
• Missing return statement
• Incorrect statement order or syntax

3. Given this Python code using an LLM wrapper, what will be printed and returned?

class SimpleModel:
    def generate(self, prompt):
        return f"Response to: {prompt}"

def wrapper(model, prompt):
    print(f"Input prompt: {prompt}")
    result = model.generate(prompt)
    print(f"Model output: {result}")
    return result

model = SimpleModel()
output = wrapper(model, "Hello")
print(f"Final output: {output}")

medium

A. Input prompt: Hello
Model output: Response to: Hello
Final output: Response to: Hello

B. Model output: Response to: Hello
Input prompt: Hello
Final output: Response to: Hello

C. Final output: Response to: Hello
Input prompt: Hello
Model output: Response to: Hello

D. Input prompt: Hello
Final output: Response to: Hello
Model output: Response to: Hello

Solution

1. Step 1: Trace the wrapper function calls

   The wrapper first prints the input prompt, then calls model.generate which returns a string, then prints the model output, and finally returns the result.

2. Step 2: Check the order of prints and final output

   The prints happen in order: input prompt, model output, then outside the wrapper the final output is printed.

3. Final Answer:

   Input prompt: Hello Model output: Response to: Hello Final output: Response to: Hello -> Option A

4. Quick Check:

   Print order matches Input prompt: Hello Model output: Response to: Hello Final output: Response to: Hello [OK]

Hint: Follow print statements in code order to find output [OK]

Common Mistakes:

• Mixing print order
• Confusing return value with print output
• Ignoring the final print outside wrapper

4. This code tries to wrap an LLM call but has an error. What is the error?

def wrapper(model, prompt):
    print('Calling model')
    output = model.generate(prompt)
    print('Output:', output)

model = SomeModel()
result = wrapper(model, 'Test')

medium

A. The wrapper function does not return the model output

B. The model object is not defined

C. The print statements have syntax errors

D. The prompt argument is missing in the wrapper call

Solution

1. Step 1: Check the wrapper function's return behavior

   The wrapper prints messages and calls model.generate but does not return the output, so result will be None.

2. Step 2: Verify other parts of the code

   The model is assumed defined as SomeModel(), print statements are correct, and the prompt is passed correctly.

3. Final Answer:

   The wrapper function does not return the model output -> Option A

4. Quick Check:

   Missing return in wrapper = The wrapper function does not return the model output [OK]

Hint: Always return model output from wrapper to use it outside [OK]

Common Mistakes:

• Forgetting to return output from wrapper
• Assuming print returns value
• Confusing variable names

5. You want to create an LLM wrapper that formats the prompt by adding a prefix, logs the prompt and output, and caches results to avoid repeated calls. Which approach best combines these features?

hard

A. Write separate functions for formatting, logging, and caching and call them outside the wrapper

B. Create a wrapper class with methods to format, log, and cache results internally

C. Modify the original model's generate method to add formatting and logging

D. Use a global variable to store all prompts and outputs without wrapping

Solution

1. Step 1: Understand the need for combining features in one place

   To keep code organized and flexible, a wrapper class can hold formatting, logging, and caching together.

2. Step 2: Evaluate options for maintainability and clarity

   Create a wrapper class with methods to format, log, and cache results internally uses a class to encapsulate all features, making it easy to manage. Write separate functions for formatting, logging, and caching and call them outside the wrapper scatters logic outside, making code messy. Modify the original model's generate method to add formatting and logging changes the model itself, which is not recommended. Use a global variable to store all prompts and outputs without wrapping uses globals, which is error-prone.

3. Final Answer:

   Create a wrapper class with methods to format, log, and cache results internally -> Option B

4. Quick Check:

   Wrapper class for combined features = Create a wrapper class with methods to format, log, and cache results internally [OK]

Hint: Use a class wrapper to keep related features together [OK]

Common Mistakes:

• Changing the original model code
• Scattering logic outside wrapper
• Using global variables for caching