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4.2 Fine-tuning and LLMOps: operational implementation

Once the architecture is in place, how do we ensure the model remains performant and maintainable over time? This is where fine-tuning (specialization) and LLMOps (operational management) come in.

4.2.1 Fine-tuning LLMs

Fine-tuning involves slightly re-training a generic model on a specific dataset to specialize it for a precise task. Unlike RAG (which provides information), fine-tuning modifies the model's "behavior" or "style".

Why fine-tune?

  • To learn a proprietary code syntax.
  • To adopt a specific reporting format.
  • To reduce latency (a small specialized model is faster than a large generic model).

Red thread: MagicFridge

The team uses a very specific internal testing language called "FrigoScript". Public models (Gemini 3) do not know it and make syntax errors.

The action: The AI engineer trains a small Open Source model (e.g., Llama 3) with 5000 examples of existing "FrigoScript" scripts.

The result: This new "GUS-Coder" model has become a world expert in FrigoScript, much better than Gemini 3 for this specific task, and cheaper to run.

4.2.2 LLMOps (Large Language Model Operations)

LLMOps is the application of DevOps principles to LLMs. It is the set of practices to deploy, monitor, and maintain models in production.

Key LLMOps activities for testing:

  1. Deployment: Making the model available to testers (API, Server).
  2. Monitoring: Verifying that the AI does not drift, become slower, or start hallucinating after an update.
  3. Cost management: Tracking token consumption to avoid budget explosion.

Red thread: MagicFridge

The QA Lead's LLMOps dashboard: She monitors GUS indicators in real-time:

  • Cost per day: $15 (Stable).
  • Average response time: 2.5 seconds (Rising, alert!).
  • User feedback: The rate of "Thumbs down" on GUS responses has increased by 10% since the last model update.

Thanks to LLMOps, she decides to roll back to the previous version of the model before it impacts the team too much.

🎓 Syllabus point (key takeaways)

  • Fine-tuning: Model specialization via light re-training. Useful for style compliance or specific languages.
  • RAG vs. Fine-tuning: RAG provides knowledge (facts), fine-tuning adapts behavior (form).
  • LLMOps: Discipline encompassing deployment, monitoring (performance, quality, cost), and maintenance of AI models.


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