Build AI Workflows, Not Just Prompts
Real AI value comes from full systems—input cleaning, structured outputs, retrieval, validation, storage, and automation—around models, not isolated prompts. Start with small, boring problems.
Shift from Prompts to Complete AI Systems
A model response alone isn't a product; it delivers no ongoing value without surrounding infrastructure. To make AI useful, wrap LLMs in workflows that handle input cleaning (e.g., normalizing data before feeding to the model), structured outputs (parsing JSON or schemas for reliability), retrieval (pulling relevant context via RAG), validation (checking outputs against rules), storage (persisting results in databases), and automation (triggering via cron jobs or APIs). This systems approach turns flashy demos into tools that solve daily problems, like automating report generation or code reviews, rather than one-off generations.
Trade-off: Prompts feel fast and exciting initially, but they lead to brittle, non-scalable results. Full systems take more upfront engineering but compound value over time, reducing manual work by 80%+ in repetitive tasks based on hands-on builds.
Solve Small, Boring Problems First
High-impact AI projects emerge from mundane pains, not grand visions. Target issues like data entry duplication, email triage, or log analysis—these have clear inputs/outputs and quick feedback loops. For example, build a script that cleans messy CSV inputs, queries an LLM for summaries, validates facts against a knowledge base, and stores results in a sheet. This beats chasing viral demos because small wins validate the workflow fast, iterate based on real use, and scale naturally.
Why it works: Boring problems have low stakes for experimentation, precise success metrics (e.g., time saved per run), and immediate ROI. Avoid hype-driven builds; they distract from production-ready automations that actually ship.