Integrating AI capabilities into a product is technically straightforward. Building something that works reliably, scales affordably, and handles the ways AI systems fail — that requires more careful thinking.
Start with the failure cases
Before building any AI feature, ask: what happens when the model gets it wrong? For many applications, the answer has major design implications.
If the model suggests a product recommendation and gets it wrong, the user gets an unhelpful suggestion. If the model fills in a legal document and gets it wrong, you have a real problem.
Classify your use case by failure cost:
- Low failure cost: Suggestions, recommendations, draft generation. Users verify the output before acting.
- Medium failure cost: Automated emails, summaries, classifications that inform decisions.
- High failure cost: Autonomous actions, financial calculations, medical or legal content.
For high-failure-cost applications, design human review into the system from the start.
The interface between AI and users
A common mistake is treating an LLM call as a black box that returns the right answer. Users will get wrong answers. The system needs to communicate uncertainty and enable correction.
Design choices that reduce the cost of AI errors:
- Show the source of AI-generated information (what document, what data)
- Make it easy for users to edit or reject AI suggestions
- Use confidence-based UI (show "I'm not sure" when uncertainty is high)
- Provide fallbacks when AI fails (fall back to search, or to a human queue)
Choosing the right AI approach
Not every problem needs a large language model. Common approaches in order of increasing complexity:
Rule-based classification: If you're classifying text into a small set of categories with clear rules, regex or simple keyword matching may be sufficient and more reliable.
Fine-tuned classifiers: Small models fine-tuned for specific classification tasks outperform large LLMs on those tasks and cost a fraction of the price.
Embedding-based search: For semantic search or recommendation, embedding models + vector search often work better and cheaper than generating answers with an LLM.
LLM with retrieval (RAG): For answering questions against a knowledge base, retrieve relevant documents and provide them as context.
LLM with tools: For tasks requiring multiple steps, calculations, or real-time data, give the model tools to call.
Fine-tuned LLM: For consistent tone, format, or domain-specific behavior that prompting cannot reliably achieve.
Start simple. Evaluate whether simpler approaches meet requirements before reaching for LLMs.
Cost and latency
LLM API calls are not free, and for high-volume applications, cost can become significant quickly.
Key levers:
- Model selection: Smaller models (GPT-4o-mini, Claude Haiku) cost 10–50x less than flagship models and are adequate for many tasks.
- Caching: Cache responses to identical or semantically similar queries.
- Prompt optimization: Shorter prompts cost less. Eliminate instructions that don't improve outputs.
- Streaming: Return tokens as they're generated rather than waiting for the full response, improving perceived latency.
Measure actual API costs in staging before going to production. Usage patterns often surprise.
Prompt management
Prompts are code — they should be versioned, tested, and reviewed like any other code. Embedding prompts directly in application code is a common early mistake.
A practical setup:
- Store prompts in a configuration file or dedicated prompt store
- Version prompts alongside model version and model configuration
- Test prompts on a representative evaluation set before deploying changes
- Log prompt/response pairs in production (with appropriate privacy controls) for debugging and evaluation
Evaluation
AI systems are harder to test than deterministic code. The output for a given input can vary, and "correctness" is often subjective. Despite this, you need evaluation to make confident changes.
Approaches:
- Golden set evaluation: Curate a set of inputs with expected outputs and run new prompts/models against them
- LLM-as-judge: Use a capable LLM to evaluate outputs against criteria
- Human evaluation: Sample production outputs and have humans rate quality
- A/B testing: Run variants in production and measure user-facing metrics
Combine these approaches. Golden sets catch regressions; human evaluation catches things the golden set doesn't cover.
Observability in production
Standard application monitoring (uptime, error rates, latency) is necessary but not sufficient. You also need:
- Input/output logging: Sample and store prompt/response pairs for debugging
- Token usage tracking: Understand cost by feature and user segment
- Latency by model and operation: Identify bottlenecks
- Error categorization: Distinguish model errors from API failures from timeout issues
Summary
Building reliable AI applications requires thinking carefully about failure cases before building, choosing the simplest approach that works, designing for human review when failure costs are high, managing prompts as versioned code, evaluating systematically, and observing what actually happens in production. The AI capability is often the easy part; the reliability engineering is where most of the work happens.