There's a pattern we see constantly with AI product builds: a founder reads a paper, watches a demo, and decides their product needs a fine-tuned model, a vector database, an orchestration layer, and a custom training pipeline — before they've shipped a single feature to a single user.
The result is an AI product that takes six months to build and zero users to validate it.
Start with the boring version first
Before you reach for LangChain, RAG, or anything resembling MLOps, ask yourself: what's the dumbest version of this AI feature that would still be useful? More often than not, a well-engineered prompt to GPT-4o or Claude will get you to a working prototype in a week — and that prototype is what you need to validate the assumption.
We've built AI products both ways. The ones that shipped and got users always started from a minimal prompt-based prototype. The ones that failed typically started with architecture diagrams.
When you actually need RAG
Retrieval-augmented generation (RAG) is the right tool when your LLM needs to answer questions about documents, data, or knowledge that didn't exist when the model was trained — and when accuracy matters more than speed.
If your AI feature is answering questions about your own product documentation, legal contracts, or internal knowledge bases, RAG is probably the right architecture. But RAG is not a silver bullet. You need clean data, good chunking strategies, meaningful embeddings, and a retrieval layer that actually surfaces relevant context.
The most common RAG failure we see: garbage in, garbage out. Teams spend months on the retrieval architecture and zero time cleaning and structuring their source documents. The AI confidently gives wrong answers because the source material is inconsistent or contradictory.
LLM selection isn't your biggest decision
OpenAI vs Anthropic vs Google vs Mistral — this is the question everyone asks, and it matters far less than you think at the MVP stage. Pick the one with the best API reliability and the clearest pricing for your use case. Switch later if you need to.
What actually matters early: your prompt structure, your context window strategy, your error handling, and your fallback behaviour when the model produces unexpected output. Ship the feature first, benchmark models second.
AI UX is a distinct discipline
Streaming responses, loading states, confidence indicators, graceful degradation when the model is wrong — AI UX has its own patterns and failure modes that don't map neatly to standard product UX.
Users need to understand when they're talking to AI, what the model knows and doesn't know, and what to do when it makes a mistake. This isn't about disclaimers — it's about designing trust intelligently. An AI feature with no escape hatch is an AI feature users will stop using after the first bad output.
The infrastructure question
You don't need a GPU cluster. You don't need a vector database with 99.99% uptime. You don't need a model monitoring platform.
For an early-stage AI product, you need: a reliable inference API, a simple way to log inputs and outputs for debugging, and a feedback mechanism so users can flag bad outputs. That's it. Scale the infrastructure when the usage data tells you to.
What we actually do with clients
Our typical AI product engagement looks like this: one week of discovery and prompt experimentation, two weeks to build a prototype that real users can interact with, and then a structured iteration cycle based on actual usage data.
We've shipped AI features into production on this timeline more times than we can count. The key is staying disciplined about what you're validating at each stage — and resisting the urge to build the final architecture before you know what the product actually needs to do.