Our Production Approach: Hybrid LLM + Small ML Models

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Our Production Approach: Hybrid LLM + Small ML Models

Reliable, efficient, and built for precise, repeatable tasks.

We already run this approach in production. For each narrowly defined task, we deploy small, specialized ML models that deliver predictable and fast results. Large Language Models (LLMs) are used upstream to help generate training data, label edge cases, and propose features, so the downstream models stay lightweight and robust.

  • More reliable outputs: small models are constrained, validated, and optimized per task (extraction, scoring, deduping, copy variants, etc.).
  • LLM-assisted training: LLMs bootstrap datasets, enrich labels, and suggest candidate features; we then validate with human-in-the-loop and tests.
  • Cost & speed: inference is primarily done by the small models → low latency and predictable costs.
  • Deterministic APIs: every step is exposed via typed endpoints with schema validation and guardrails.
  • Continuous evaluation: golden sets, drift checks, and automated QA keep quality steady in production.

How it Works

  1. Define a narrow task → target schema.
  2. Use LLM to expand/clean labels & edge cases.
  3. Train a compact model (classifier/ranker/extractor).
  4. Wrap in a strict API with validators.
  5. Monitor with golden tests & drift alerts.

Where We Apply It

  • Field extraction & normalization
  • Candidate/job matching scores
  • Duplicate detection
  • Ad copy generation & variant ranking
  • Compliance & rule checks

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