“How do we add AI to our product?” comes up in every client call this year. Most teams either bolt on a chat widget that nobody uses, or spend months building an “AI strategy” that ships nothing. There’s a better way — six concrete patterns that actually move metrics, that I’ve shipped to production over the last 18 months.
1. Smart defaults and auto-fill
The cheapest, highest-ROI use of an LLM: pre-fill forms, suggest defaults, generate first drafts. Users edit before submitting, so quality bar is low — but you cut the time-to-first-action by 60–80%.
Examples that work: auto-generated product descriptions, AI-suggested email subjects, auto-tagged tickets, pre-filled invoice line items from a vague text prompt.
Implementation: one Claude or GPT-4o-mini call, 100ms response, results streamed into the field. No fine-tuning, no fancy infrastructure. €0.50–€5 per 1,000 users per month.
2. Document understanding (RAG done right)
Users upload a PDF, contract, spreadsheet, or knowledge base. AI extracts structured data, answers questions about it, or summarizes it. The technical pattern: embed the document with a fast model, store in pgvector or Pinecone, retrieve relevant chunks at query time, pass to an LLM.
Pitfalls: cheap chunking strategies degrade quality fast. Plan for proper boundary-aware splitting, reranking on retrieval, and citing sources in the answer.
When to use: any product that handles documents — legal, HR, finance, support docs, contracts, manuals.
3. Content generation with brand guardrails
Marketing teams want AI to write social posts, blog drafts, email copy. The naive version produces obviously-AI text that hurts your brand. The good version: provide brand voice examples, audience definitions, and a writing style guide as a system prompt, then let users tweak the output.
The trick is feedback loops — let users mark which outputs they keep vs trash, and feed that signal back into the prompt or RAG context. Quality compounds.
4. Conversational search inside your product
Instead of clicking through five filter menus, users type “show me invoices from Q1 over €500 that weren’t paid on time”. The LLM converts that into SQL or a structured query, returns results, lets you refine.
Critical: never let the model run raw SQL. Use a schema-constrained query builder, or function calls with strict typed parameters. One careless prompt-injection bug = full DB read.
When it works, it changes how power-users feel about your product. They stop asking for new filters because the chat just answers.
5. Workflow agents (carefully)
Multi-step tasks the user used to do manually: “fetch all overdue invoices, draft reminder emails per client tier, queue them for tomorrow morning”. This is where agents shine — but only when you constrain them with a finite tool set and human approval at key steps.
Avoid: fully autonomous agents that take destructive actions. Always require a human “OK, send” before anything leaves your system or touches money.
Build in: thorough logs, undo for everything, dollar/quota limits per session, and an alert for the dev team when an agent hits an edge case it can’t handle.
6. AI that learns from your usage data
The most underrated pattern: don’t call an LLM at all. Use the structured data you already have to score, rank, or predict. “Likely to churn”, “best lead to call next”, “products to recommend”, “anomalous transaction” — most of these are gradient-boosted-tree problems, not LLM problems.
An LLM costs $5/1k requests. A trained XGBoost model on your data costs $0 per request and runs in microseconds. Smart products mix both.
How to start without wasting money
- Pick one pattern that matches a real user pain point.
- Ship the cheapest version possible — Claude Haiku or GPT-4o-mini, no RAG, no fancy infra.
- Measure: time saved, task completion rate, retention. Numbers, not vibes.
- If users love it, then invest in proper evals, prompt versioning, model fallbacks, and observability.
- If users don’t use it, kill it and move on. Most AI features fail in the first two weeks — that’s data, not failure.
Where most AI features die
- The “chat with our docs” widget that nobody opens. Build search, not chat.
- The autonomous agent that sometimes deletes data and nobody trusts.
- The AI feature buried three menus deep nobody knows exists.
- The proof-of-concept that never gets prompt versioning, evals, or monitoring — degrades silently in six months.
Need help shipping a real AI feature?
If you’re sitting on a product where AI could move a metric you care about — but you don’t know where to start — drop me an email at contact@veylodev.com. I’ll do a free 30-minute review of your product and tell you which of the six patterns above fits, and how cheap a first version would be to ship.