ML Model Optimization • Model Caching (Embedding Cache, Result Cache)Medium⏱️ ~3 min
Cache Key Design and Canonicalization for High Hit Rates
Proper cache key design is the difference between 5 percent and 40 percent hit rates. The key must capture everything that affects the response while stripping volatile tokens that do not change intent.
A stable cache key concatenates multiple components. Model identifier ensures you do not serve GPT 3.5 responses when using GPT 4. Prompt template hash captures system instructions and formatting. Sampling parameters like temperature and top p matter because temperature 0.7 and 0.0 produce different outputs. User or tenant context prevents cross contamination. Tool configuration hash covers which functions or APIs the model can call. Locale and safety settings prevent serving English answers to Spanish prompts or unsafe content in strict modes. For semantic caches, add embedding model identifier and preprocessing version to avoid geometry mismatches after model updates.
Canonicalization strips noise. Lowercase where semantics allow, normalize whitespace and punctuation, remove timestamps and request identifiers that change every call but do not affect intent. For multi step prompts, factor out the stable instruction block and vary only the user content portion. A well tuned system can see exact hit rates jump from 8 percent to 28 percent just by better normalization, because greetings like hi there versus hello or trailing punctuation differences no longer fragment the keyspace.
The failure mode is accidental sharing. If you forget to include tenant identifier, one customer sees another customer's data. If you omit model version, a cache populated by an old model serves stale logic after deployment. If you skip tool configuration, a prompt that should trigger a database lookup returns a cached response that assumed no tools. Production systems use a checklist: model, template, parameters, tenant, locale, safety, tools, embedding version. Miss any and you introduce either false sharing or false misses.
💡 Key Takeaways
•Include model identifier, prompt template hash, sampling parameters (temperature, top p), tenant or user segment, locale, safety settings, and tool configuration in every cache key. Missing any component risks false sharing or cache misses.
•Canonicalization strips volatile tokens like timestamps, request IDs, and greeting variations. Normalize whitespace, lowercase where appropriate, and factor out stable instruction blocks. This can increase exact hit rates from 8 to 28 percent.
•For semantic caches, namespace by embedding model version and preprocessing logic version. Changing the embedding model or normalization changes vector space geometry, making old cache entries yield poor matches with new queries.
•Multi step prompts benefit from factoring. Keep the system instruction and template stable in the key, vary only the user provided content. This maximizes reuse across conversations with different user inputs but same instructions.
•Use write through for exact caches with high confidence in correctness. Use write around for semantic caches, only admitting entries that pass validators and quality checks to avoid amplifying hallucinations or policy violations.
•Two tier lookup improves hit rates. Check exact match first (0.3 to 2ms in memory), then check semantic tier (5 to 20ms with vector search). Maintain both indices and enforce metadata alignment beyond vector similarity for the semantic tier.
📌 Examples
A customer support bot strips timestamps and request IDs from prompts, lowercases where semantics allow, and normalizes whitespace. Exact hit rate increases from 12 to 31 percent, saving $22K monthly in API costs at 8 million requests.
An internal tool uses this key format: sha256(model_id + template_hash + json.dumps(sorted(params)) + tenant_id + locale + canonical_prompt). This ensures deterministic ordering and prevents accidental cross tenant leakage.
After an embedding model upgrade from 768 to 1536 dimensions, a semantic cache without version namespacing shows hit rate collapse from 18 to 3 percent and false positive spike to 12 percent. Adding model version to the key isolates old and new entries.