LLMs vs Traditional Machine Learning

A traditional ML model is a specialist: trained for one task, it outputs a label, number or box. An LLM is a generalist: it takes text (and sometimes images or audio) and generates new content. Specialists are usually smaller, faster and more predictable. Generalists handle open-ended language tasks no specialist can.

The core difference

Comparison

Which to use: examples

Combining both

Many good features chain a specialist and a generalist:

class ReceiptParser(
    private val recognizer: TextRecognizer = TextRecognition.getClient(TextRecognizerOptions.DEFAULT_OPTIONS),
    private val llm: ReceiptLlm,                       // your Gemini-backed extractor
) {
    suspend fun parse(image: InputImage): Receipt? {
        val text = recognizer.process(image).await().text      // fast, on-device OCR
        if (text.isBlank()) return null
        return llm.extractReceipt(text)                        // JSON with total, date, merchant
            ?.takeIf { it.total >= 0 }                          // validate model output
    }
}

Decision checklist

1. Is there a ready-made ML Kit or MediaPipe solution? Start there.
2. Is the output a fixed set of labels or numbers? Prefer a task model.
3. Does the task need open-ended language understanding or generation? Consider an LLM.
4. Are latency, offline use or cost critical? Favour on-device and smaller models.
5. Can you tolerate occasional wrong outputs? If not, add validation or avoid LLMs. See Evaluating AI Responses.

Frequently asked questions

Conclusion

Use traditional ML for well-defined tasks with structured outputs (fast, cheap, predictable) and LLMs for open-ended language tasks. Combine them, so specialists perceive and generalists reason, and validate LLM outputs.

Next step: compare Google's LLM options in Gemini Nano vs Cloud-Based Gemini Models.