How to Use LLMs to Automate Your Business Workflows
A practical guide to integrating large language models into real business processes — without the hype, just the results.
A practical guide to integrating large language models into real business processes — without the hype, just the results.
Why LLMs finally ship
For a long stretch, "AI workflows" meant a demo on Tuesday and a support ticket on Friday. What changed in 2024 is the maturity of the surrounding infrastructure: structured output, function calling, caching, evals, and enough observability to treat an LLM like any other production dependency. The model is no longer the bottleneck — the scaffolding around it is.
The RAG trap
Retrieval-Augmented Generation is the default reach for teams adopting LLMs, and most of them end up with a vector database and a new set of problems. RAG is right when the answer lives in a corpus too large to prompt with. It is wrong when the corpus is small, the schema is known, or the real ask is "do a thing," not "retrieve a thing." We reach for direct tool use first, and only add retrieval when measurement forces it.
Evals are the product
We write the eval harness before we write the prompt. Without one, you cannot tell whether a change improved your system or merely shifted its failure modes. Bad evals are worse than no evals: they produce the feeling of progress. Good evals are boring, deterministic, and owned by someone who will lose sleep when they drop.
Where it pays back
The projects with the best payback aren't the most impressive — they're the most repetitive. Contract review, lead triage, support-ticket routing, document classification. Anywhere a human is reading the same kind of content over and over and making a small structured decision, there is room for an LLM with evals to hit 90%+ accuracy and reclaim a meaningful fraction of someone's week.
What to build first
Pick the narrowest task that is genuinely annoying, has clear correct-answer criteria, and would be useful even at 70% accuracy. Ship that, measure it, and iterate. Do not build a platform. Do not build a framework. Build the thing, then let the second and third use cases teach you what to generalize.