The patterns in this catalogue have been extracted from a single production system, Zeeguu, in the language-learning domain. We have argued throughout that these forces arise from the LLM’s own properties (per-token cost, multi-second latency, non-determinism, imprecision, a rapidly shifting provider landscape, and general-purpose capability) meeting the demands of a live application, neither of which is specific to language learning, and that the patterns should therefore generalise to other interactive applications that surface LLM-generated text to end users. Whether this is borne out in practice is an empirical question we cannot fully answer from a single case study. The most direct way to validate or refute the catalogue is to gather complementary examples (and counter-examples) from practitioners working in other domains. A PLoP writers’ workshop is one venue for exactly this kind of community refinement; mining open-source repositories for additional instances of these patterns (and patterns we missed) is a natural follow-up.
A second limitation is that the catalogue reports patterns we have found useful but does not yet quantify their impact systematically. Cost and latency improvements are reported informally for some patterns; a more rigorous deployment-level evaluation (measuring, for example, how much of Zeeguu’s LLM bill is attributable to which patterns, or how much user-perceived latency racing providers removes) is future work.
A further direction is to treat the catalogue as the seed of a pattern language rather than a flat list. The patterns are not independent: they compose (pre-computation feeds Prompt Amortization; LLM Output Provenance flags which artifacts Soft Invalidation of LLM Artifacts should retire) and they evolve over a system’s lifetime (an LLM may begin as a Rent, Then Build stand-in, hand off to a specialized tool, and remain as the Escalate to the LLM fallback). Documenting these interactions, sequences, and tensions, so a designer can navigate from one pattern to the next, is a contribution beyond the individual patterns.
These choices are not made once. A system’s pattern mix is actively revised as it scales and the provider landscape shifts: Zeeguu began by making single LLM calls and only recently added parallel, raced ones, and may later drop that approach or fold it together with per-user budgeting as usage grows. A longitudinal account of how and why a system’s chosen patterns change over time would deepen the lifecycle dimension that this single-snapshot catalogue can only sketch.
Finally, we expect the catalogue to be incomplete1: further patterns will likely surface only through engagement with practitioners working at different scales, in different domains, and with different LLM providers.
Some proto-patterns already in progress are collected under Candidate Patterns.
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The patterns in this paper are a selection from a larger, evolving catalogue maintained online at https://mircealungu.github.io/llm-integration-patterns/ ↩