Checklist — Generative AI: what can it do for your business?
Step-by-step guide
A practical checklist to launch your first production-ready generative AI integration — from task definition to production monitoring.
Define one specific, measurable task
Resist expanding scope. Pick one workflow — drafting replies, summarising reports, or answering FAQs. Define what success looks like: time saved, acceptance rate, or error rate.
Audit your data sources
List which documents, databases, or APIs contain the context the model needs. Remove obsolete versions, identify personal-data risks, and check access permissions before sending anything to an external API.
Choose your model and deployment type
Evaluate hosted APIs (OpenAI, Anthropic, Google) versus private deployment (Ollama, Azure Private). For confidential data, prefer private. Consider cost per token at your expected volume.
Build a test set before writing production code
Collect 30–50 real examples with known correct answers. Include edge cases — ambiguous requests, missing context, adversarial inputs. Run your prompt variants against this set before deploying.
Add a human review step before go-live
For consequential outputs (customer-facing, legal, financial), require a person to approve before sending. Track approval rates — below 70 % means the prompt or data quality needs work.
Monitor cost, latency, and output quality in production
Set spending alerts (daily and monthly). Log input/output pairs for quality audits. Track latency — users notice responses above 3 seconds. Review a random sample of outputs weekly.
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