AI Integration

Why AI integration fails more often than it should

The failure mode isn't usually technical — it's not being clear about what problem the AI is actually solving. "Add AI to the product" is not a feature. LLMs hallucinate. They're non-deterministic. They're expensive to run at scale. Integrating them thoughtfully requires understanding their capabilities and limitations, not just calling an API and hoping for the best.

The projects that succeed treat AI as a tool with specific, well-defined jobs — summarize this document, extract these fields, answer questions about this knowledge base, classify this input. When the scope is narrow, the quality is high and the failure modes are manageable. When the scope is "make it smart," you get a product that's unreliable and expensive.

Good AI integration is mostly about prompt engineering, retrieval strategy, and fallback behavior — not model architecture.

How I integrate AI into products

I start with the use case and work backwards to the implementation. No cargo-culting the latest research paper.

• OpenAI and Anthropic APIs with structured outputs, function calling, and cost controls baked in
• Retrieval-Augmented Generation (RAG) with vector search via Pinecone or pgvector for knowledge-grounded answers
• Streaming responses so users see output immediately instead of waiting for full completion
• Evals and logging to measure quality over time and catch regressions when models update

The result is an AI feature that does exactly what it promises, costs a predictable amount to run, and degrades gracefully when the model is uncertain.