Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

The biggest bottleneck in enterprise fine-tuning is rarely GPU compute—it is waiting months for domain experts to manually write thousands of high-quality training examples. Synthetic data generation turns internal documents and a small seed set into rich, verified SFT and preference datasets using frontier teacher models.

Hands-on labs cover document-grounded Q&A synthesis, Evol-Instruct and persona-driven diversity scaling, rejection sampling with deterministic and judge-based verifiers, and building scalable pipelines with Distilabel and batch APIs. Participants generate a verified synthetic fine-tuning dataset from their own domain documents.

Who Should Attend?

  • Data scientists and AI engineers fine-tuning models in low-data enterprise domains
  • ML engineers distilling frontier model capabilities into smaller open-weight models
  • Data engineers building automated training and evaluation data pipelines
  • Prerequisite: Python and working with LLM APIs

Key Takeaways

  • Synthesise grounded instruction-response pairs directly from internal manuals, policies and logs
  • Prevent mode collapse and repetitive phrasing using topic, constraint and persona matrices
  • Distil step-by-step reasoning traces and chosen/rejected preference pairs from teacher models
  • Filter out hallucinated or low-quality synthetic rows via rejection sampling, code execution and LLM judges
  • Build cost-efficient, reproducible synthetic data pipelines with Distilabel and batch inference APIs

Curriculum

Synthetic Data Paradigms and Model Collapse Risks
  • Seed amplification (Self-Instruct), document-grounded synthesis and knowledge distillation
  • Preventing lexical and semantic homogenization, and navigating model licence terms
Engineering Diversity: Evol-Instruct, Magpie and Persona Matrices
  • Combinatorial prompt generation across domain taxonomies, difficulty tiers and user personas
  • Synthesising multi-hop reasoning and unanswerable/abstention questions from internal document chunks
Synthesising Reasoning Traces and Preference Pairs
  • Rejection sampling (STaR): generating chain-of-thought rationales and keeping only verified answers
  • Constructing DPO chosen/rejected pairs via controlled perturbations and weaker checkpoints
Verification, Filtering and Distilabel Pipelines
  • Deterministic verifiers: JSON schema validation, SQL/Python execution and source grounding checks
  • Scoring faithfulness, complexity and educational value with calibrated LLM judges
  • Scaling generation cheaply with Distilabel, vLLM and asynchronous batch APIs
Workshop: Document-to-Dataset Synthesis Pipeline
  • Building an end-to-end synthesis and filtering pipeline over a sample enterprise corpus
  • Auditing embedding diversity and answer accuracy of the resulting dataset

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