Our LLM API invoice was rising 30% month-over-month. Site visitors was rising, however not that quick. After I analyzed our question logs, I discovered the true drawback: Customers ask the identical questions in numerous methods.
"What's your return coverage?," "How do I return one thing?", and "Can I get a refund?" have been all hitting our LLM individually, producing practically equivalent responses, every incurring full API prices.
Actual-match caching, the plain first resolution, captured solely 18% of those redundant calls. The identical semantic query, phrased otherwise, bypassed the cache completely.
So, I applied semantic caching based mostly on what queries imply, not how they're worded. After implementing it, our cache hit price elevated to 67%, decreasing LLM API prices by 73%. However getting there requires fixing issues that naive implementations miss.
Why exact-match caching falls quick
Conventional caching makes use of question textual content because the cache key. This works when queries are equivalent:
# Actual-match caching
cache_key = hash(query_text)
if cache_key in cache:
return cache[cache_key]
However customers don't phrase questions identically. My evaluation of 100,000 manufacturing queries discovered:
-
Solely 18% have been precise duplicates of earlier queries
-
47% have been semantically just like earlier queries (similar intent, totally different wording)
-
35% have been genuinely novel queries
That 47% represented large price financial savings we have been lacking. Every semantically-similar question triggered a full LLM name, producing a response practically equivalent to 1 we'd already computed.
Semantic caching structure
Semantic caching replaces text-based keys with embedding-based similarity lookup:
class SemanticCache:
def __init__(self, embedding_model, similarity_threshold=0.92):
self.embedding_model = embedding_model
self.threshold = similarity_threshold
self.vector_store = VectorStore() # FAISS, Pinecone, and many others.
self.response_store = ResponseStore() # Redis, DynamoDB, and many others.
def get(self, question: str) -> Elective[str]:
"""Return cached response if semantically related question exists."""
query_embedding = self.embedding_model.encode(question)
# Discover most related cached question
matches = self.vector_store.search(query_embedding, top_k=1)
if matches and matches[0].similarity >= self.threshold:
cache_id = matches[0].id
return self.response_store.get(cache_id)
return None
def set(self, question: str, response: str):
"""Cache query-response pair."""
query_embedding = self.embedding_model.encode(question)
cache_id = generate_id()
self.vector_store.add(cache_id, query_embedding)
self.response_store.set(cache_id, {
'question': question,
'response': response,
'timestamp': datetime.utcnow()
})
The important thing perception: As an alternative of hashing question textual content, I embed queries into vector area and discover cached queries inside a similarity threshold.
The brink drawback
The similarity threshold is the vital parameter. Set it too excessive, and also you miss legitimate cache hits. Set it too low, and you come incorrect responses.
Our preliminary threshold of 0.85 appeared cheap; 85% related needs to be "the identical query," proper?
Flawed. At 0.85, we bought cache hits like:
-
Question: "How do I cancel my subscription?"
-
Cached: "How do I cancel my order?"
-
Similarity: 0.87
These are totally different questions with totally different solutions. Returning the cached response could be incorrect.
I found that optimum thresholds fluctuate by question kind:
|
Question kind |
Optimum threshold |
Rationale |
|
FAQ-style questions |
0.94 |
Excessive precision wanted; incorrect solutions injury belief |
|
Product searches |
0.88 |
Extra tolerance for near-matches |
|
Help queries |
0.92 |
Stability between protection and accuracy |
|
Transactional queries |
0.97 |
Very low tolerance for errors |
I applied query-type-specific thresholds:
class AdaptiveSemanticCache:
def __init__(self):
self.thresholds = {
'faq': 0.94,
'search': 0.88,
'help': 0.92,
'transactional': 0.97,
'default': 0.92
}
self.query_classifier = QueryClassifier()
def get_threshold(self, question: str) -> float:
query_type = self.query_classifier.classify(question)
return self.thresholds.get(query_type, self.thresholds['default'])
def get(self, question: str) -> Elective[str]:
threshold = self.get_threshold(question)
query_embedding = self.embedding_model.encode(question)
matches = self.vector_store.search(query_embedding, top_k=1)
if matches and matches[0].similarity >= threshold:
return self.response_store.get(matches[0].id)
return None
Threshold tuning methodology
I couldn't tune thresholds blindly. I wanted floor reality on which question pairs have been really "the identical."
Our methodology:
Step 1: Pattern question pairs. I sampled 5,000 question pairs at varied similarity ranges (0.80-0.99).
Step 2: Human labeling. Annotators labeled every pair as "similar intent" or "totally different intent." I used three annotators per pair and took a majority vote.
Step 3: Compute precision/recall curves. For every threshold, we computed:
-
Precision: Of cache hits, what fraction had the identical intent?
-
Recall: Of same-intent pairs, what fraction did we cache-hit?
def compute_precision_recall(pairs, labels, threshold):
"""Compute precision and recall at given similarity threshold."""
predictions = [1 if pair.similarity >= threshold else 0 for pair in pairs]
true_positives = sum(1 for p, l in zip(predictions, labels) if p == 1 and l == 1)
false_positives = sum(1 for p, l in zip(predictions, labels) if p == 1 and l == 0)
false_negatives = sum(1 for p, l in zip(predictions, labels) if p == 0 and l == 1)
precision = true_positives / (true_positives + false_positives) if (true_positives + false_positives) > 0 else 0
recall = true_positives / (true_positives + false_negatives) if (true_positives + false_negatives) > 0 else 0
return precision, recall
Step 4: Choose threshold based mostly on price of errors. For FAQ queries the place incorrect solutions injury belief, I optimized for precision (0.94 threshold gave 98% precision). For search queries the place lacking a cache hit simply prices cash, I optimized for recall (0.88 threshold).
Latency overhead
Semantic caching provides latency: You could embed the question and search the vector retailer earlier than realizing whether or not to name the LLM.
Our measurements:
|
Operation |
Latency (p50) |
Latency (p99) |
|
Question embedding |
12ms |
28ms |
|
Vector search |
8ms |
19ms |
|
Complete cache lookup |
20ms |
47ms |
|
LLM API name |
850ms |
2400ms |
The 20ms overhead is negligible in comparison with the 850ms LLM name we keep away from on cache hits. Even at p99, the 47ms overhead is suitable.
Nonetheless, cache misses now take 20ms longer than earlier than (embedding + search + LLM name). At our 67% hit price, the maths works out favorably:
-
Earlier than: 100% of queries × 850ms = 850ms common
-
After: (33% × 870ms) + (67% × 20ms) = 287ms + 13ms = 300ms common
Web latency enchancment of 65% alongside the fee discount.
Cache invalidation
Cached responses go stale. Product data adjustments, insurance policies replace and yesterday's appropriate reply turns into right now's incorrect reply.
I applied three invalidation methods:
-
Time-based TTL
Easy expiration based mostly on content material kind:
TTL_BY_CONTENT_TYPE = {
'pricing': timedelta(hours=4), # Modifications steadily
'coverage': timedelta(days=7), # Modifications hardly ever
'product_info': timedelta(days=1), # Each day refresh
'general_faq': timedelta(days=14), # Very steady
}
-
Occasion-based invalidation
When underlying knowledge adjustments, invalidate associated cache entries:
class CacheInvalidator:
def on_content_update(self, content_id: str, content_type: str):
"""Invalidate cache entries associated to up to date content material."""
# Discover cached queries that referenced this content material
affected_queries = self.find_queries_referencing(content_id)
for query_id in affected_queries:
self.cache.invalidate(query_id)
self.log_invalidation(content_id, len(affected_queries))
-
Staleness detection
For responses which may turn into stale with out express occasions, I applied periodic freshness checks:
def check_freshness(self, cached_response: dict) -> bool:
"""Confirm cached response remains to be legitimate."""
# Re-run the question in opposition to present knowledge
fresh_response = self.generate_response(cached_response['query'])
# Examine semantic similarity of responses
cached_embedding = self.embed(cached_response['response'])
fresh_embedding = self.embed(fresh_response)
similarity = cosine_similarity(cached_embedding, fresh_embedding)
# If responses diverged considerably, invalidate
if similarity < 0.90:
self.cache.invalidate(cached_response['id'])
return False
return True
We run freshness checks on a pattern of cached entries every day, catching staleness that TTL and event-based invalidation miss.
Manufacturing outcomes
After three months in manufacturing:
|
Metric |
Earlier than |
After |
Change |
|
Cache hit price |
18% |
67% |
+272% |
|
LLM API prices |
$47K/month |
$12.7K/month |
-73% |
|
Common latency |
850ms |
300ms |
-65% |
|
False-positive price |
N/A |
0.8% |
— |
|
Buyer complaints (incorrect solutions) |
Baseline |
+0.3% |
Minimal improve |
The 0.8% false-positive price (queries the place we returned a cached response that was semantically incorrect) was inside acceptable bounds. These instances occurred primarily on the boundaries of our threshold, the place similarity was simply above the cutoff however intent differed barely.
Pitfalls to keep away from
Don't use a single world threshold. Totally different question sorts have totally different tolerance for errors. Tune thresholds per class.
Don't skip the embedding step on cache hits. You is likely to be tempted to skip embedding overhead when returning cached responses, however you want the embedding for cache key era. The overhead is unavoidable.
Don't neglect invalidation. Semantic caching with out invalidation technique results in stale responses that erode consumer belief. Construct invalidation from day one.
Don't cache every part. Some queries shouldn't be cached: Personalised responses, time-sensitive data, transactional confirmations. Construct exclusion guidelines.
def should_cache(self, question: str, response: str) -> bool:
"""Decide if response needs to be cached.""
# Don't cache personalised responses
if self.contains_personal_info(response):
return False
# Don't cache time-sensitive data
if self.is_time_sensitive(question):
return False
# Don't cache transactional confirmations
if self.is_transactional(question):
return False
return True
Key takeaways
Semantic caching is a sensible sample for LLM price management that captures redundancy exact-match caching misses. The important thing challenges are threshold tuning (use query-type-specific thresholds based mostly on precision/recall evaluation) and cache invalidation (mix TTL, event-based and staleness detection).
At 73% price discount, this was our highest-ROI optimization for manufacturing LLM programs. The implementation complexity is average, however the threshold tuning requires cautious consideration to keep away from high quality degradation.
Sreenivasa Reddy Hulebeedu Reddy is a lead software program engineer.
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