In order for stable, cost-efficient, low latency automation systems to operate efficiently, caching intermediate results generated by the LLM is necessary. By incorporating a well-planned caching layer into automation workflows that involve repetitive tasks or processes, automating repetitive functions can eliminate the excess use of model calls and therefore increase the overall throughput of the entire automation system. The general approach used by many experienced AI engineers to create a technical approach for developing a cache layer includes the following information:
1. Determining Input Hashing
Set up a hashing strategy that generates a stable hash from the normalized input. To normalize the input, sort the keys, eliminate unnecessary text, standardize the formatting and eliminate noise from the input. The resulting hash will be used as the cache key. With this process, every time the same normalized input generates an output in cache, it does not duplicate the output in cache using a slightly different format.
2. Correct Cache Layer Selection
When selecting a cache layer, consider the workload characteristics of the automation processes.
- Redis or Memcached are well-suited for real-time or high-frequency tasks.
- SQLite or DuckDB works for local or edge automation.
- Cloud object stores, such as S3 or GCS, are helpful when the workflow involves large or infrequently accessed results.
3. Cache Versioning
Introduce a version field within each key. Whenever the prompt template, system message, reasoning flow, or model version changes, bump the version. This prevents old cached outputs from being reused when the logic has evolved.
4. Step Level Caching
Each step or stage of the automation pipeline will have its own cache, including: Summarisation, Extraction, Validation, Classification, Planning, and Transformation. If a later stage is altered, previous stages will not have been affected, saving substantial computing on repeated executions.
5. Cache Invalidation Rules
It is essential to define an initial TTL for cache entries that hold dynamic information. Additionally, purge triggers for business rule updates (i.e., updated business processes) should be set up manually. Lastly, it is important to implement automated eviction policies for cache entries that are either stale or are infrequently accessed. Monitoring cache hit ratios, as well as cache misses, will help assess how efficiently the caching system stores LLM calls.
6. Scalability and Workflow Integration
Complex multi-agent workflows often need distributed or hierarchical caching patterns. When designing such systems, teams frequently consult an AI automation agency to architect caching pipelines that operate reliably across microservices and high-volume workloads.
A properly implemented caching system could result in a 40% - 70% reduction in LLM Calls and consistent performance across repeated automation cycles.
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