When to Use an LLM (and When Not To)

There’s a pattern that shows up in almost every team that starts experimenting with LLMs: initial excitement, a flurry of use cases, and then a reckoning with the fact that some of those use cases probably didn’t need an LLM at all.

That’s not a failure — it’s a normal part of figuring out where a new technology actually fits. This post is designed to help you skip that detour and think clearly from the start about when an LLM is the right choice and when it isn’t.

The Core Question

An LLM is fundamentally a tool for working with language. So the first question to ask is simple: does this problem involve reading, understanding, or generating text in a meaningful way?

If the answer is no — if the problem is about crunching numbers, querying a database, making rule-based decisions — an LLM is probably the wrong tool. Start there before you go any further.

When LLMs Are a Good Fit

The input is unstructured text. Emails, documents, support tickets, meeting transcripts, contracts, logs — these are LLM territory. The core value of an LLM is precisely that it can extract meaning from text that doesn’t conform to a predefined schema.

The task requires language understanding. Classification, summarization, entity extraction, sentiment analysis, question answering — these are tasks where the nuance of language matters and where rule-based approaches break down at scale.

Variability in the input is expected. If users will phrase the same underlying request in dozens of different ways, a rigid rule-based system will require constant maintenance. LLMs handle natural language variation gracefully.

You need a first draft, not a final answer. LLMs are excellent at accelerating human work — generating a starting point that a human then reviews and refines. Contract drafts, report summaries, email responses, onboarding documents — anywhere that human review is part of the workflow, LLMs can cut the time dramatically.

The cost of an occasional error is manageable. LLMs are probabilistic. They will sometimes be wrong. If the task has a human in the loop to catch mistakes, that’s often fine. If the output goes directly into a high-stakes automated process without review, you need to be more careful.

When LLMs Are the Wrong Tool

You need precise, deterministic outputs. If you need the same input to always produce exactly the same output, LLMs are a poor fit. They’re non-deterministic by design.

You need to query structured data. If your question can be answered by a SQL query or a lookup in a database, do that. Using an LLM as a proxy for a database introduces unnecessary cost and error surface.

Accuracy is critical and unverifiable. LLMs can produce confident-sounding incorrect answers. In domains where an undetected error is a serious problem — medical dosing, financial calculations, legal filings — you need either a deterministic system, a strong grounding mechanism (like RAG with source citations), or robust human review.

The problem is fundamentally mathematical. Arithmetic, statistical modeling, numerical optimization — LLMs are not reliable calculators. Use the right tool.

You have no way to evaluate quality. If you can’t measure whether the LLM’s output is good or not, you can’t improve it, catch regressions, or build trust in the system. This isn’t a reason not to use an LLM, but it is a reason to invest in evaluation before you invest in deployment.

A Decision Framework

When evaluating a potential LLM use case, run through these questions:

1. Does this involve language understanding or generation? If not, stop here and reach for a different tool.
2. What happens when the output is wrong? If the answer is “a human catches it,” proceed. If the answer is “it goes directly into a consequential system,” think carefully about guardrails.
3. Is there a simpler, more reliable approach? A regex, a database query, a rule-based classifier — if these can do the job, they’re often faster, cheaper, and easier to audit.
4. Can you measure quality? Define what “good” looks like before you build. You’ll need this later.
5. Will this use case scale? If you’re thinking about deploying at volume, factor in latency, cost per call, and reliability requirements early.

The Hybrid Reality

Most production AI systems don’t use LLMs in isolation. They use them as one component in a larger pipeline: LLMs handle the language-understanding steps, deterministic systems handle business logic and data retrieval, and humans review the outputs that matter most.

That hybrid architecture is usually more reliable, more auditable, and more cost-effective than trying to make an LLM do everything.

At Komposer, we’ve built our platform around exactly this model — agents that use LLMs where language understanding is the value-add, and integrate with your existing data and systems to do the rest. The goal is never to replace your tools with AI. It’s to make every part of your workflow smarter.

The teams that get the most out of LLMs are the ones who think clearly about when to use them — and when not to.