AI Workflow Evaluation: How to Test Prompt and Model Changes Before Release
A practical release-testing method for AI workflows: build representative cases, check real outcomes, compare versions and define when human review is required.
Technical playbooks, software engineering benchmarks, AI research, and performance marketing insights from the SBS engineering & growth teams.
11 articles
A practical release-testing method for AI workflows: build representative cases, check real outcomes, compare versions and define when human review is required.
Evaluate AI fraud signals against reviewed outcomes, not a headline accuracy figure. Measure false positives, delayed labels and review capacity before using scores to interrupt transactions.
Define qualified leads using observable business criteria before adding AI scoring. Evaluate missed opportunities, false positives and handoff quality with examples reviewed by your sales team.
Use CRM automation to standardize fields and flag possible duplicates before merging them. Preserve original values, relationship history and a recovery path for uncertain corrections.
Choose RAG for evidence retrieval and consider fine-tuning for defined behavior changes. Compare evaluated prototypes, source freshness and maintenance work before committing proprietary data.
Treat LLM tool calls as proposed business actions. Validate arguments, enforce permissions outside the model and record verified step results so interrupted workflows can resume safely.
Choose one customer decision for personalization to improve. Test useful defaults, catalogue eligibility, recommendation diversity and downstream outcomes before expanding an AI recommendation system.
Build enterprise RAG around approved sources and document permissions. Test retrieval, answer support and freshness separately, including questions the knowledge base cannot answer.
Build voice support around clear tasks and reliable handoffs. Test unclear speech, interruptions, verification and failed helpdesk calls so customers can reach a person with their context intact.
Define the value window, costs and observation date before predicting customer LTV. Compare mature cohorts and future-period results so incomplete histories do not become revenue promises.
Map data, permissions and retention across enterprise AI workflows. Assign owners, test disclosure and action boundaries, and keep evidence current as models, tools and sources change.
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