Designing AI-Powered Features

A lot of system design discussions today include AI by default. Recommendations, chatbots, summaries, assistants, everything seems to need a model behind it. But in real systems, the hardest part is not adding AI. The hardest part is adding it without breaking the system, the latency budget, or the user experience.

Many teams overcomplicate AI features because they start with the model instead of the problem. They jump to fine-tuning, agents, or complex pipelines before understanding where AI actually fits in the system.

This lesson focuses on designing AI-powered features in a practical way—using AI where it adds value, keeping the system simple, and making sure it behaves safely in production.

Where AI Fits in Real Systems

AI works best when it augments decision-making rather than replacing the entire system. In practice, most AI features fall into a small number of categories.

Classification is one of the most common. This includes spam detection, content moderation, sentiment analysis, or intent detection. The output is usually a label or category that the rest of the system uses to decide what to do next.

Ranking is another common use case. Search results, recommendations, feeds, and prioritization systems often rely on AI to decide order rather than absolute correctness. These systems tolerate some imperfection as long as overall quality improves.

Summarization is increasingly popular. Systems generate short versions of long documents, meeting notes, tickets, or chat histories. These features usually assist users rather than drive core logic.

Finally, there are agent-like systems, where AI plans steps, calls tools, or coordinates actions. These are powerful but also the riskiest and hardest to control, especially in early-stage systems.

A useful rule of thumb is this: the closer AI is to core correctness (payments, permissions, irreversible actions), the more conservative the design should be.

Latency-Sensitive AI vs Batch AI

One of the most important design decisions is whether an AI feature runs in real time or asynchronously.

Latency-sensitive AI runs in the user request path. Examples include autocomplete, instant recommendations, live chat responses, or ranking search results. These systems must respond quickly and consistently. Even small delays can hurt user experience.

Because of this, latency-sensitive AI systems often use smaller models, caching, aggressive timeouts, and strict fallbacks. If the model is slow or unavailable, the system must degrade gracefully.

Batch AI works differently. It runs offline or asynchronously and processes data in the background. Examples include nightly recommendations, periodic summaries, retraining datasets, analytics, or fraud detection pipelines.

Batch systems allow heavier models, longer processing times, and retries. They are easier to scale and safer to operate, which is why many production AI systems start as batch workflows.

A common beginner mistake is forcing everything into real-time AI. In many cases, batch processing delivers most of the value with far less risk.

Prompting, Fine-Tuning, and RAG: Choosing the Right Tool

Not every AI feature needs training. In fact, most don’t.

Prompting is the simplest approach. You provide instructions and examples at runtime and let the model respond. Prompting works well for summarization, transformation, classification, and many conversational tasks. It is fast to iterate and easy to change.

Fine-tuning is useful when the task is very specific, repetitive, and stable. It helps when prompts become too long or inconsistent, or when you need predictable behavior. Fine-tuning increases maintenance cost and should only be used when prompting clearly falls short.

Retrieval-Augmented Generation, or RAG, is used when the model needs access to external or up-to-date knowledge. Instead of embedding all information into the model, the system retrieves relevant documents and feeds them into the prompt. This is common for internal knowledge bases, documentation search, or domain-specific assistants.

A practical design approach is to start with prompting, move to RAG when knowledge becomes the bottleneck, and consider fine-tuning only when behavior needs to be locked down.

AI Is Not the Source of Truth

One of the most important design principles is that AI outputs should rarely be treated as absolute truth.

Models can be wrong, incomplete, or inconsistent. Production systems should treat AI responses as suggestions or signals, not final authority.

For example, an AI classifier might flag content as risky, but a rules-based system or human review may make the final decision. A recommendation system may propose items, but business logic still applies constraints.

Keeping AI as a supporting component makes systems safer and easier to reason about.

Building Safe Fallback Behavior

Every AI-powered feature must have a fallback.

Models can time out. APIs can fail. Outputs can be low quality. If the system collapses when AI fails, it is not production-ready.

Good fallback behavior is simple and predictable. If AI ranking fails, fall back to a default sort. If summarization fails, show the original content. If an AI assistant is unavailable, provide a basic search or static response.

Timeouts are critical. The system should never wait indefinitely for an AI response. After a defined limit, it should move on.

Logging and monitoring AI failures is also important. Silent failures are worse than visible ones.

Designing AI Features That Age Well

AI systems change faster than traditional software. Models improve, costs change, and capabilities evolve.

Good system design isolates AI components behind clear interfaces. The rest of the system should not depend on model-specific details.

This allows you to swap models, change providers, or adjust strategies without rewriting the entire application.

It also makes experimentation safer. You can test AI features gradually, measure impact, and roll back if needed.

Final Thoughts

Designing AI-powered features is not about adding the most advanced model. It is about adding intelligence in a way that respects system constraints, user experience, and operational reality.

The best AI systems are often invisible. They make products better without becoming the product itself.

In system design interviews, interviewers are not looking for buzzwords. They want to see that you understand where AI helps, where it hurts, and how to build systems that remain reliable when AI inevitably misbehaves.

If you can explain how your system works with AI and without it, you are designing at a production level.