Caching strategies for LLMs in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main purpose of caching in Large Language Models (LLMs)?

Caching in LLMs is used to store previous computations or outputs to speed up future requests, reducing response time and saving computational resources.

intermediate

Explain token-level caching in LLMs.

Token-level caching saves the hidden states or outputs for each token generated so that when generating the next token, the model can reuse these cached states instead of recomputing from scratch.

intermediate

What is the difference between short-term and long-term caching in LLMs?

Short-term caching stores recent computations during a single session or request to speed up immediate next steps, while long-term caching saves outputs or embeddings across sessions to reuse for repeated queries or similar inputs.

beginner

How does caching help reduce latency in LLM applications?

By reusing previously computed results, caching avoids repeating expensive calculations, which lowers the time the model takes to respond, thus reducing latency for users.

advanced

Name a challenge when implementing caching strategies for LLMs.

One challenge is managing cache invalidation, ensuring that cached data stays relevant and accurate when inputs or model parameters change.

What does token-level caching store in LLMs?

A Hidden states of tokens generated

B Raw input text

C Final output only

D Model weights

Token-level caching stores the hidden states of tokens to reuse during next token generation.

Which caching type is used to speed up repeated queries across sessions?

A Short-term caching

B Long-term caching

C Token-level caching

D No caching

Long-term caching saves outputs or embeddings across sessions for repeated queries.

Why is cache invalidation important in LLM caching?

A To keep cached data accurate and relevant

B To increase cache size

C To speed up training

D To reduce model size

Cache invalidation ensures that outdated or incorrect cached data is removed or updated.

Caching in LLMs primarily helps to:

A Increase model size

B Add more training data

C Reduce response time

D Change model architecture

Caching reduces response time by reusing previous computations.

Which of the following is NOT a benefit of caching in LLMs?

A Lower latency

B Reduced computation cost

C Faster response for repeated inputs

D Improved model accuracy

Caching improves speed and cost but does not directly improve model accuracy.

Describe how token-level caching works in Large Language Models and why it is useful.

Think about how the model generates text one token at a time.

Explain the challenges involved in managing cache invalidation for LLM caching strategies.

Consider what happens if the model or input changes but the cache is not refreshed.