Caching strategies for LLMs in Prompt Engineering / GenAI - Model Metrics & Evaluation

Metrics & Evaluation - Caching strategies for LLMs

Which metric matters for caching strategies in LLMs and WHY

For caching strategies in large language models (LLMs), the key metrics are cache hit rate and latency reduction. Cache hit rate measures how often the model can reuse previous results instead of recomputing, saving time and resources. Latency reduction shows how much faster the model responds due to caching. These metrics matter because caching aims to speed up responses and reduce computation cost without losing accuracy.

Confusion matrix or equivalent visualization

Instead of a confusion matrix, caching strategies use a cache hit/miss table to track performance:

Cache Accesses: 1000
Hits: 750
Misses: 250

Cache Hit Rate = Hits / (Hits + Misses) = 750 / 1000 = 75%
    

This shows 75% of requests were served from cache, reducing computation.

Precision vs Recall tradeoff analogy for caching

In caching, the tradeoff is between cache size and freshness. A bigger cache stores more results (higher hit rate) but may keep outdated info. A smaller cache updates faster but misses more hits. For example:

• Large cache: High hit rate, but some responses may be stale.
• Small cache: Low hit rate, but always fresh results.

Choosing the right balance depends on how often the model's outputs change and how critical fresh answers are.

What "good" vs "bad" metric values look like for caching in LLMs

Good caching:

• Cache hit rate above 70% means most requests reuse results.
• Latency reduction of 30% or more speeds up user experience.
• Minimal accuracy loss from stale cached outputs.

Bad caching:

• Cache hit rate below 30% means caching is ineffective.
• Little to no latency improvement.
• High error rate due to outdated cached responses.

Common pitfalls in caching metrics

• Ignoring accuracy impact: High cache hit rate is useless if cached answers are wrong or outdated.
• Data leakage: Caching sensitive or user-specific data can cause privacy issues.
• Overfitting cache: Caching too aggressively may cause the model to repeat old answers even when context changes.
• Measuring only hits: Not tracking latency or accuracy can hide poor user experience.

Self-check question

Your LLM caching system has a 98% cache hit rate but users report outdated answers often. Is this caching good for production? Why or why not?

Answer: No, because although the cache hit rate is very high, the cached answers are stale and reduce accuracy. This harms user trust and experience. The caching strategy needs to balance hit rate with freshness to be effective.

Key Result

Cache hit rate and latency reduction are key metrics to evaluate caching effectiveness in LLMs, balancing speed and answer freshness.

Practice

1. What is the main purpose of caching in large language models (LLMs)?

easy

A. To save previous answers and avoid repeating work

B. To increase the size of the model

C. To change the model's training data

D. To make the model forget old information

Solution

1. Step 1: Understand caching concept

   Caching stores previous results so the system can reuse them instead of recalculating.

2. Step 2: Apply to LLMs context

   In LLMs, caching saves time and resources by reusing answers for repeated inputs.

3. Final Answer:

   To save previous answers and avoid repeating work -> Option A

4. Quick Check:

   Caching = Save and reuse answers [OK]

Hint: Caching means saving past answers to reuse them [OK]

Common Mistakes:

• Thinking caching changes model size
• Confusing caching with training data updates
• Believing caching deletes old info

2. Which Python tool is commonly used for simple caching in LLM applications?

easy

A. os.listdir

B. functools.lru_cache

C. math.sqrt

D. random.shuffle

Solution

1. Step 1: Identify caching tools in Python

   functools.lru_cache is a built-in decorator for caching function results.

2. Step 2: Check other options

   random.shuffle shuffles lists, math.sqrt calculates square roots, os.listdir lists files; none cache results.

3. Final Answer:

   functools.lru_cache -> Option B

4. Quick Check:

   Python caching tool = lru_cache [OK]

Hint: lru_cache is Python's simple caching decorator [OK]

Common Mistakes:

• Choosing random.shuffle as caching
• Confusing math functions with caching
• Picking file system functions

3. Given this Python code using a dictionary cache for LLM responses, what will be printed?

cache = {}
def get_response(input_text):
    if input_text in cache:
        return cache[input_text]
    response = f"Answer for {input_text}"
    cache[input_text] = response
    return response

print(get_response('hello'))
print(get_response('hello'))

medium

A. None\nAnswer for hello

B. Answer for hello\nNone

C. Error: KeyError

D. Answer for hello\nAnswer for hello

Solution

1. Step 1: Analyze first call get_response('hello')

   Cache is empty, so it creates 'Answer for hello', stores it, and returns it.

2. Step 2: Analyze second call get_response('hello')

   Input is in cache, so it returns cached 'Answer for hello' without recomputing.

3. Final Answer:

   Answer for hello Answer for hello -> Option D

4. Quick Check:

   Cache hit returns saved answer [OK]

Hint: Cache returns saved answer on repeated input [OK]

Common Mistakes:

• Assuming second call returns None
• Expecting error on repeated key
• Thinking cache clears automatically

4. This code tries to cache LLM outputs but has a bug. What is the error?

cache = {}
def get_response(input_text):
    if input_text in cache:
        return cache[input_text]
    response = f"Answer for {input_text}"
    cache = {input_text: response}
    return response

print(get_response('test'))
print(get_response('test'))

medium

A. Cache is reset each call, losing previous entries

B. generate_answer function is undefined

C. Syntax error in dictionary assignment

D. Infinite recursion in get_response

Solution

1. Step 1: Check cache update line

   cache = {input_text: response} replaces whole cache dict, losing old data.

2. Step 2: Understand effect on repeated calls

   Each call resets cache, so repeated inputs are not cached properly.

3. Final Answer:

   Cache is reset each call, losing previous entries -> Option A

4. Quick Check:

   Cache replaced, not updated [OK]

Hint: Use cache[key] = value to update, not assign new dict [OK]

Common Mistakes:

• Thinking generate_answer is missing
• Assuming syntax error in dict
• Believing recursion happens

5. You want to cache partial results of LLM calls to speed up responses when inputs share common prefixes. Which caching strategy best fits this need?

hard

A. Use random sampling to cache some inputs

B. Cache only full input strings as dictionary keys

C. Use a trie (prefix tree) to store cached outputs by input prefixes

D. Clear cache after every call to save memory

Solution

1. Step 1: Understand prefix sharing in inputs

   Inputs sharing prefixes can reuse partial results if cached by prefix.

2. Step 2: Identify suitable data structure

   A trie (prefix tree) efficiently stores and retrieves data by prefixes, ideal for this case.

3. Final Answer:

   Use a trie (prefix tree) to store cached outputs by input prefixes -> Option C

4. Quick Check:

   Prefix caching = trie structure [OK]

Hint: Trie caches shared prefixes efficiently [OK]

Common Mistakes:

• Caching only full inputs misses prefix reuse
• Random caching is inefficient
• Clearing cache wastes saved data