Training Data for Specialist LLMs

Overview

The quality of a fine-tuned model is almost entirely determined by the quality of its training data. This section covers formatting standards, data sourcing strategies, and the specific data types needed to produce a credible network-engineer expert model.

Entries

  • Dataset Formats — Alpaca, ShareGPT, ChatML: when to use each and how to structure examples
  • Network Engineering Training Data — The specific data types, sources, and coverage needed for a Juniper/Cisco expert model
  • Synthetic Data Generation — Using a large LLM to generate training data at scale, writing effective generation prompts per competency, estimating domain expert review cost, and filtering systematic errors

Entries

  • Dataset Formats for Fine-Tuning — The three standard dataset formats used for LLM instruction fine-tuning — Alpaca, ShareGPT, and ChatML — with examples and guidance on when to use each.
  • Network Engineering Training Data — The specific categories, sources, and volume of training data needed to produce a credible network-engineer expert model for Juniper and Cisco devices — covering command generation, configuration, troubleshooting, and tool interpretation.
  • Synthetic Training Data Generation — How to generate synthetic training data for a network engineer specialist model at scale — using a capable LLM as a data generator, writing effective generation prompts, estimating the domain expert review cost, and maintaining quality across large datasets.