AI features have moved from experimental add-ons to core parts of many products. Search, recommendations, content generation, and anomaly detection are increasingly powered by models rather than hand-written rules. For engineering teams, the challenge is integrating these capabilities in a way that is reliable, observable, and maintainable.
Successful AI projects treat models as just one part of a larger system. Data quality, evaluation, and surrounding application logic usually have more impact on user experience than a small increase in model accuracy.
Common AI Patterns in Web Applications
Most teams do not need to train models from scratch. Instead, they compose existing APIs and foundation models into product-specific workflows. A few integration patterns cover the majority of real-world use cases.
- Retrieval-augmented generation for question answering and support, where content is fetched from a knowledge base and provided to a language model.
- Ranking and recommendations that personalise content or product lists based on user behaviour.
- Classification and routing to sort tickets, flag risky activity, or triage incoming requests.
Each pattern has clear boundaries where traditional deterministic logic still applies: permissions checks, rate limiting, and input validation remain outside the model.
Data, Evaluation, and Feedback Loops
Models depend heavily on the data they see. Logging inputs and outputs, anonymising where necessary, and wiring feedback into the pipeline are essential to keep behaviour aligned with product goals.
- Capture representative examples of real traffic for offline evaluation.
- Define simple, repeatable metrics that correlate with user value for each feature.
- Review edge cases regularly rather than waiting for a major incident.
Evaluation does not have to be complex. For many applications, a combination of automatic checks and periodic human review is enough to identify regressions and inform improvements.
Operational Considerations
AI features share many operational concerns with other backend services: latency, throughput, cost, and resilience. Remote APIs introduce additional factors such as rate limits and quota management, while self-hosted models require capacity planning and GPU scheduling.
- Set explicit latency and cost budgets for AI calls in each user flow.
- Design clear fallbacks for when models are unavailable or responses are low confidence.
- Monitor token usage, error rates, and user behaviour alongside traditional application metrics.
Thinking about these constraints early avoids surprises when an experimental feature becomes popular and traffic grows beyond initial expectations.