LLM-generated features for traditional ML

Use this when an LLM seems useful, but using it on every live request feels too expensive, too slow, or too operationally fragile.

The short answer: one of the best uses for LLMs is offline feature generation. Let the LLM create labels, summaries, or semantic signals in batch, then let a cheaper downstream model or rules system use those features at serving time.

What you will get in 9 minutes

• A practical way to use LLMs off the hot path
• Good examples of LLM-generated features
• When this pattern beats direct LLM inference
• A worksheet for spotting strong candidates in your own stack

Use this when

• You have recurring classification or ranking problems
• Latency matters in the live path
• Data quality is messy, but the live decision must stay cheap
• You want to use LLMs without turning every request into an inference event

The 60-second answer

Use LLMs to create:

• labels
• summaries
• semantic attributes
• weak supervision signals

Then use those outputs as inputs to:

• traditional classifiers
• ranking models
• business rules
• analytics pipelines

This often gives you more of the “intelligence” benefit with less recurring cost.

What counts as an LLM-generated feature

Examples:

• support conversation summary used by a churn model
• normalized product taxonomy label used in search ranking
• account health sentiment used in prioritization
• extracted risk flags used in compliance review
• semantic topic tags used in recommendations

The LLM is not the end user product in these cases. It is part of the data preparation layer.

When this pattern wins

This pattern is strong when:

• the feature can be generated in batch
• the output is reused many times
• a cheaper downstream model can consume the feature
• the live path needs predictability

It is especially attractive when the same expensive interpretation would otherwise happen repeatedly on every request.

When it does not

This pattern is weaker when:

• the feature goes stale too quickly
• the task needs live reasoning from fresh context
• the downstream system cannot use the generated signal well
• the feature is too subjective to validate

Good implementation pattern

1. Define the feature contract.
2. Generate the feature on a labeled sample.
3. Evaluate agreement with humans or business outcomes.
4. Backfill in batch.
5. Feed the feature into the cheaper downstream model or rules layer.

Examples of feature contracts:

• intent = billing | bug | feature_request
• risk_score = 1..5
• summary = short normalized account summary
• topic_tags = array of controlled taxonomy labels

Why this is often better economics

Direct LLM inference charges you on the hot path forever.

Feature generation can shift that cost into:

• nightly batches
• ingest-time enrichment
• one-time backfills

Then the live system uses:

• cheaper models
• vector lookups
• deterministic rules
• classic ML

That can be a major margin improvement for high-volume workflows.

How to evaluate the feature

Do not stop at “the feature looks reasonable.”

Measure:

• agreement with human labels
• downstream model lift
• stability over time
• batch cost per record
• refresh cadence needed

The right question is not “did the LLM produce something interesting?” It is “did this feature improve the downstream system enough to justify the generation cost?”

Copyable feature opportunity worksheet

For one workflow, answer:

1. What expensive live interpretation is happening repeatedly?
2. Could that interpretation be precomputed?
3. Would a downstream model or rules system benefit from it?
4. How often would the feature need to refresh?
5. What metric would prove it helped?

If the answer to question 2 is yes and question 4 is not “every request,” this pattern is worth testing.

Common failure modes

• generating features with no downstream consumer
• no evaluation of feature usefulness
• over-refreshing features that rarely change
• using free-text features where controlled labels would be better
• assuming offline generation is automatically cheap without measuring batch volume

How StackSpend helps

This pattern changes spend shape from live inference to batch enrichment. Tracking costs by workflow helps teams see whether they are actually moving cost out of the hot path or just adding a second layer of AI spend.

What to do next

• Structured outputs for extraction, classification, and scoring
• Evaluation playbook for LLM applications