I had access to dozens of models and the temptation was to test everything. I learned that overengineering came from chasing tiny accuracy gains on noncritical tasks. My current process is simple: classify the task (NLP, summarization, classification), pick a small set of candidates (one compact, one mid, one high accuracy), and run three quick tests with canned inputs. I measure latency, cost, and consistent failures.
If the compact model meets the acceptance criteria, I stop. If not, I step up. I also lock in the model choice in the project spec so switching isn’t casual.
How do you shortlist and sanity check models before they become part of your automation pipeline?
I do quick blind tests to compare responses and cost. If a smaller model meets the bar, I keep it.
When I need many models available without juggling keys, I pick and swap inside Latenode. That lets me test alternatives fast and revert if something breaks.
I create a short benchmark suite of 20 examples and score each model on stability, not just peak accuracy. Models that give consistent outputs across runs are often better for pipelines. Also I pay attention to latency under load; some models spike and that breaks real time automations.
My rule is to pick the simplest model that passes a small, focused test set. I build that test set from real user queries and a few edge cases I expect. Then I run a cost vs benefit check: does the higher accuracy reduce manual work or approvals enough to justify the cost? Often it does not. In one case we replaced a high cost model with a cheaper variant and added a single lightweight classifier to catch the worst errors. The hybrid reduced cost by 70 percent and kept SLA intact. The key is instrumenting the chosen model in production with monitoring and a fallback path—if the model confidence falls below a threshold, route to a lightweight human check instead of overengineering.
I treat model selection as an engineering decision. Define the acceptance metric first. Run a small A/B test under representative throughput. Evaluate not only accuracy but behavior under edge inputs and latency tail. If a model fails under realistic load or produces brittle outputs, it’s a poor fit even if its average scores are higher. Also plan a rollback path and a monitoring metric tied to business outcome. Those measures prevent model changes from becoming scope creep.