What it takes to move an AI demo into a reliable product: evaluation, reliability, observability, security, cost control and ownership.
Why demos are misleading
An AI prototype can look impressive after a few days of work because it is tested on friendly examples. Production means handling every kind of input, every day, at a predictable cost. Most of the work lies in that gap.
Define success before scaling
Agree what good looks like in measurable terms: task success rate, acceptable error types, response time and cost per task. Build the evaluation set described in our evaluation guide and use it as the release gate.
Reliability
Model APIs can be slow, rate-limited or temporarily unavailable. Add timeouts, retries with backoff, fallbacks to another model or a simpler path, and graceful error messages. Validate structured outputs and retry or repair when they fail.
Observability
Log each request with its prompt version, model, retrieved context, tool calls, output, latency, token usage and user feedback. Tracing tools built for LLM applications make it possible to see exactly why a particular answer was produced.
Security and data handling
Apply the controls in our security guide: permission-aware retrieval, least-privilege tools, output validation and review of provider data retention terms. Document what data leaves your infrastructure and why.
Cost control
Set budgets per feature, cache repeated requests where safe, route simple tasks to smaller models, and cap output length. Monitor cost per successful task rather than cost per token.
Versioning and change management
Treat prompts, retrieval settings and model versions as code: version them, review changes and run the evaluation suite before each release. Model providers update and retire models, so plan for regular re-testing.
Human oversight and ownership
Decide where people review outputs, how users can report problems and who owns the system after launch. Content, prompts and evaluation sets all need an owner to stay accurate over time.
Key takeaways
- Define measurable success before scaling.
- Plan for failures in the model API and in model output.
- Log enough to explain any answer after the fact.
- Version prompts and models, and re-test on every change.
If you have a working AI prototype and need to take it to production, our team can review what is needed and help build it.
AI Consulting Services- Hidden Technical Debt in Machine Learning SystemsSculley et al., Google (NeurIPS) · 2015 · Research paperproceedings.neurips.cc
- MLOps: Continuous delivery and automation pipelines in machine learningGoogle Cloud · Architecture guidecloud.google.com
- Semantic conventions for generative AI systemsOpenTelemetry · Specificationopentelemetry.io