AI in Your Product: Where It Actually Helps
Practical ways to integrate AI—semantic search, chat, automation—without overreaching or overbuilding.
AI can make your product smarter, but only if it’s solving a real problem. Here’s where it tends to work.
Search and discovery – Semantic or natural-language search over your content and data helps users find what they need without exact keywords. Good for docs, knowledge bases, and internal tools.
Conversational interfaces – Chat or “ask in plain language” works when users have many possible questions and you can ground answers in your data. Use it for support, internal Q&A, or guided workflows.
Automation and summarisation – Drafting, summarising, or classifying content (tickets, reports, feedback) can save time. Start with narrow, well-defined tasks and clear guardrails.
What to avoid – “AI for the sake of AI”, or promising human-level reasoning. Be clear about limitations and always allow a human in the loop for high-stakes decisions.
Stack – Use established APIs and models where possible; customise with prompts, RAG, and fine-tuning only when you’ve validated the use case with a simpler setup first.
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