Catalog
concept#AI#MLOps#AI Governance#Machine Learning

LLM Training

Process of training large language models by optimizing model parameters on large datasets toward defined learning objectives.

LLM training refers to the process of building or improving a large language model by optimizing its parameters on large text and, optionally, multimodal datasets.
Established
Medium

Classification

  • Medium
  • Organizational
  • Organizational
  • Intermediate

Technical context

Experiment tracking and model registryData versioning and data catalogDeployment and monitoring platform

Principles & goals

Prioritize data quality, deduplication, and leakage preventionUse reproducible training pipelines and controlled experimentsTreat evaluation, safety checks, and regression tests as gates
Iterate
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Data leakage leads to overstated performance and compliance risks
  • Bias and toxic content can distort behavior and outputs
  • Insufficient safety testing increases misuse and reputation risk
  • Strict data hygiene: deduplication, leakage checks, PII filtering
  • Reproducible runs with deterministic seeds and versioning
  • Multi-stage evaluation: quality, safety, robustness, cost

I/O & resources

  • Training data, data licenses, and data pipeline
  • Compute (GPU/TPU), training stack, and configuration
  • Target metrics, evaluation suite, and safety policies
  • Model checkpoints and release artifacts
  • Evaluation reports and regression analyses
  • Documentation, audit, and compliance artifacts

Description

LLM training refers to the process of building or improving a large language model by optimizing its parameters on large text and, optionally, multimodal datasets. It includes dataset selection and preparation, objective definition, running pretraining and fine-tuning (e.g., supervised fine-tuning), and iterative evaluation. Additional steps such as alignment (e.g., preference optimization) and safety and quality checks are often integrated to achieve desired behavior, robustness, and compliance. Effective LLM training requires reproducible pipelines, clear metrics, controlled experimentation, and awareness of risks such as data leakage, bias, hallucinations, and cost.

✔

  • Improved task performance and domain coverage through targeted training
  • More consistent behavior via alignment and policy constraints
  • Measurable quality improvements through systematic evaluation

✖

  • High cost for compute, data preparation, and iteration
  • Results strongly depend on data quality and objective definition
  • Training can introduce regressions and new failure modes

  • Loss/Perplexity

    Training and validation metrics for convergence and generalization.

  • Task Benchmarks

    Comparable metrics on defined task and evaluation suites.

  • Safety and Policy Compliance

    Meeting safety criteria and policies via tests and red-teaming.

SFT for code assistance

A base model is fine-tuned on prompt/response pairs for coding tasks and regression-tested against an evaluation suite.

Continued pretraining for domain language

A model is further pretrained on curated domain documents to better handle terminology and style.

Alignment with preference data

A model is aligned via preference optimization toward helpful and safer behavior and validated with safety benchmarks.

1

Define goals, metrics, policies, and evaluation suite

2

Curate, deduplicate, filter, and version data

3

Run training (pretraining/fine-tuning) with checkpoints

4

Run evaluation, safety tests, and regression checks

5

Establish release, deployment, monitoring, and iteration

⚠️ Technical debt & bottlenecks

  • Unversioned datasets and missing reproducibility
  • Missing model registry and unclear release artifacts
  • Ad-hoc evaluations without durable benchmark suites
Compute and GPU availabilityData quality and data curationEvaluation and regression handling
  • Training on sensitive or proprietary data without rights clearance
  • Using training data that contaminates evaluation or benchmarking
  • Releasing a model without safety validation into production contexts
  • Data leaks due to overlap across train/validation/test
  • Poor generalization due to overfitting on curated samples
  • Cost explosion due to uncontrolled experimentation
Machine learning engineering and deep learningData engineering, data curation, and quality assuranceMLOps: reproducibility, evaluation, and monitoring
Requirements for model quality, robustness, and cost controlPrivacy, IP protection, and regulatory requirementsNeed for domain-specific competence and behavior
  • • Compute budget and runtime limits
  • • Data rights, licensing, and privacy
  • • Reproducibility and auditability of training runs