Not every feature needs AI
The first question to ask isn't "which LLM should we use" — it's "does this feature actually benefit from AI?" Many tasks that get pitched as AI features are better solved with traditional search, rules engines, or even simple string matching. AI makes sense when you need: semantic understanding of unstructured text, generation of novel content, or classification that would require hundreds of hand-written rules.
The RAG pattern is overused but sometimes right
Retrieval-Augmented Generation (RAG) has become the default pattern for adding AI to applications. The idea is sound: embed your documents into a vector database, retrieve relevant chunks when a user asks a question, and feed those chunks to an LLM for synthesis. But RAG is expensive to build well, requires ongoing maintenance of your embedding pipeline, and often returns mediocre results without significant tuning. Before committing to RAG, try prompting with your full context (if it fits in the context window) — it's simpler and often more accurate.
Choosing an LLM provider
For most applications, the choice is between OpenAI (GPT-4o, GPT-4o mini), Anthropic (Claude Sonnet, Claude Haiku), and Google (Gemini). GPT-4o mini and Claude Haiku are the cost-effective choices for high-volume, lower-complexity tasks. GPT-4o and Claude Sonnet are better for complex reasoning. The differences are smaller than marketing suggests — test with your actual use cases before committing.
What it actually costs
LLM API costs are often underestimated. A typical chatbot feature serving 10K daily users at 500 tokens per interaction costs $15-50/day with GPT-4o mini. A RAG pipeline adds vector database costs ($70-200/month for Pinecone), embedding costs, and engineering time for pipeline maintenance. Budget for 3-5x your initial estimate as you tune quality and handle edge cases.
Start with the smallest useful thing
The most successful AI features start small. Instead of building a full conversational AI, start with a single AI-powered feature: auto-categorization of support tickets, smart search suggestions, or draft email generation. Ship it, measure whether users actually find it valuable, and expand from there. The biggest risk with AI features is spending months building something users don't care about.