LLM vs traditional machine learning: which should do each task?
Every new AI feature raises the same question for product and data teams: should a large language model handle it, or a trained machine learning model such as gradient boosting or logistic regression? The answer depends on the task, the data you already hold and how each output has to be explained.
Should an LLM or a trained ML model do this task?
Quick answer
LLM vs traditional machine learning is decided task by task. Use a trained ML model for scoring, forecasting and any decision you must explain with specific reasons. Use an LLM for reading, extracting and drafting language. Many ML and LLM driven solutions need both, and Agnotic Technologies builds each part and the handoff between them.
| Criterion | LLM | Trained ML model |
|---|---|---|
| Scoring rows of tabular data (risk, churn, fraud) | Weak fit: numeric features are not its strength and it does not return a calibrated probability | Best fit: needs labelled outcomes; evaluate with AUC, calibration and error by segment |
| Forecasting a number over time | Poor fit on its own; can describe a finished forecast in words | Best fit: needs a clean history per series; evaluate on later periods the model never saw |
| Sorting short text into known categories | Good fit with few or no labels; score it on a checked test set before release | Good fit once a large labelled set exists, and cheaper per prediction at high volume |
| Extracting fields from varied documents | Best fit: needs instructions, an output schema and sample documents; evaluate field by field | Only for fixed layouts, and only after many documents per layout are labelled |
| Summarizing and drafting | Best fit: needs source text and a rubric; reviewers score drafts against it | Not applicable: classic models do not write prose |
| Conversation and question answering | Best fit, grounded in retrieved documents, with a handoff when the answer is not there | Can route a question to the right queue or intent, not answer it |
| Decisions that need specific reasons | Keep it out of the decision: generated reasons can change between runs | Best fit: reasons come from the features that drove the score and can be tested |
| Very high volume or tight response times | Cost and latency grow with tokens per request, context size and model size | Cost per prediction depends on feature lookups and model size, not on text length |
What they are
What is LLM
An LLM is a general model pretrained on very large amounts of text, then steered with instructions, worked examples and retrieved context. It reads and writes language without task-specific training, and returns free text or structured text in the format you ask for.
What is Trained ML model
A traditional ML model, such as gradient boosted trees, logistic regression or a small neural classifier, is trained on your own labelled history for one prediction. It returns a score, class or number, gives the same output for the same input, and its result can be traced back to the input features.
Key differences
What data does each approach need before work starts?
A trained model needs labelled history: past cases with the outcome you want to predict, drawn from the population the feature will serve, with enough examples of the rare outcomes that matter most. Label quality is often the real project. An LLM needs no training run, but it does need clear instructions, a target output format, a few worked examples and a test set of real inputs with checked answers. Teams that skip the test set cannot tell whether the feature works.
How is each one evaluated?
A scoring model is judged on ranking quality such as AUC, on calibration (whether predicted probabilities match observed rates) and on error broken down by segment, so you see where it fails. An LLM feature is judged on an evaluation set: rubric scores per output for correctness, completeness and format, field-level accuracy for extraction, and repeated runs to measure variation. ISO/IEC 23053:2022 gives teams a shared framework and vocabulary for the parts of a machine learning system, which helps when both kinds of model sit in one pipeline.
Who can explain a decision, and to whom?
Under Regulation B, a creditor's statement of reasons for adverse action must be specific and indicate the principal reasons, and saying the applicant failed to reach a qualifying score on the creditor's scoring system is not enough (12 CFR 1002.9(b)(2)). A trained model on defined features lets you map the strongest contributing features to reason codes and test that mapping. An LLM's free-text explanation is written after the fact and may not reflect what drove the outcome, so it can draft the letter from reason codes but should not produce them.
Why do LLM errors look different from model errors?
A classifier is wrong with a number attached, so you can set a threshold and send uncertain cases to a person. An LLM can be wrong in fluent, confident text. NIST AI 600-1, the Generative AI Profile published in July 2024, names this risk confabulation: confidently stated but erroneous or false content. Checks therefore sit outside the model: schema validation, comparing extracted values against the source, required citations and review of low-confidence outputs.
What drives cost and latency per prediction?
For an LLM, the drivers are tokens in and out, how much context is retrieved per request, model size, retries and whether the model is a hosted API or self-hosted. For a trained model, they are feature computation and lookups, model size and whether scoring runs in batches or in real time. Hardware is its own decision, covered in our GPU vs CPU inference comparison.
Does an LLM make results less repeatable?
Yes, unless you design for it. A trained model returns the same score for the same input until it is retrained. LLM output can vary between runs and can change when the provider updates the model. Pin model versions, store prompts and outputs, and rerun the evaluation set on every change. Retraining schedules and drift monitoring for trained models are covered in our model drift glossary entry.
When to use each
When to use Large language model (LLM)
- Pulling fields from contracts, referral letters, invoices or emails whose layout changes from sender to sender.
- Summarizing long threads, notes or tickets for a person who reviews the draft.
- Drafting replies, descriptions or reports from facts the system already holds.
- Conversational interfaces that answer from your documents and hand off when unsure.
When to use Trained machine learning model
- Risk, propensity, fraud or churn scores computed from rows and columns of history.
- Demand, volume or staffing forecasts from time series.
- High-volume classification where each prediction has to be fast and inexpensive to run.
- Credit and similar decisions where the customer is owed specific reasons for the outcome.
Which hybrid patterns combine an LLM and a trained model?
1. LLM extracts, model scores. The LLM turns documents or messages into typed fields, validation rejects bad values, and a trained model scores the fields. The decision and its reasons come from the model.
2. Model routes, LLM drafts. A classifier sends each case to a queue or template, and the LLM drafts the reply for that route, which a person approves.
3. LLM labels, model learns. The LLM proposes labels on historical text, reviewers correct a sample, and a smaller trained classifier is fitted for high-volume use.
4. Model flags, LLM summarizes for staff. A risk or anomaly model flags a case, and the LLM writes an internal summary of the records behind the flag. The flag itself stays numeric and auditable.
5. Keep the boundary typed. In every pattern, pass structured fields between the two parts, log both sides and test each part on its own evaluation set.