Adding an LLM feature to your product sounds straightforward — call an API, display the response. The reality is that production LLM features require careful architecture across four dimensions: reliability, latency, cost, and safety. Most teams underestimate three of the four and discover this after launch when the feature starts breaking in ways unit tests cannot catch.
Start with a clear, narrow problem definition before touching any API. The worst AI projects begin with 'let's add AI' rather than 'users spend 20 minutes doing X manually — can we reduce that to two?' A narrow, measurable problem produces a better feature than a broad, aspirational one. Define success metrics before you write a single prompt.
Prompt engineering is dramatically underrated. A well-designed system prompt with clear instructions, worked examples, output format constraints, and explicit edge-case handling will outperform fine-tuning for most use cases — and costs nothing. Invest heavily here before considering anything more complex. Structured output (JSON mode or XML tags) resolves most reliability issues.
For retrieval-augmented generation, the quality of your retrieval pipeline matters more than the LLM you choose. Chunking strategy, embedding model selection, metadata filtering, and re-ranking all affect output quality significantly. A well-tuned RAG system using a smaller model often outperforms a naive implementation using a frontier model at five times the cost.
Evaluation is the part most teams skip, and it is why most AI features get quietly removed six months after launch. Build evaluation datasets from real user interactions, define clear pass/fail criteria, and run automated evals in CI before every prompt change. Without evals, you are flying blind — every prompt tweak is equally likely to help or hurt.
Safety and trust belong in your architecture from day one. Input and output filtering, rate limiting, confidence thresholds for automated actions, human-in-the-loop escalation paths, and clear user disclosure that they are interacting with AI — these are not optional extras for a production feature. They are the difference between a feature your users trust and one they stop using after the first bad experience.
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