Stage 03 · Build — Training Readiness

Training, fine-tuning & generalization readiness

Fine-tuning is not always the answer. This lab helps decide when prompting, RAG, fine-tuning, traditional ML, or a hybrid approach is appropriate — and whether the data is ready to support that decision. No model is trained here; this is readiness, decisioning, and risk only.

Recommended approach: RAG first — fine-tuning later only if neededrag

Training dataset not required — this use case is better served by RAG, evaluation datasets, and monitoring, because answers must stay grounded in current source documents.

Decision memo

Fine-tune vs RAG vs prompt

Enterprise teams should not fine-tune by default. The right approach depends on the workflow, data, freshness, governance burden, and evaluation evidence.

Why this approach

●Answers require current source evidence and citations
●Knowledge changes often; retrieval keeps it fresh
●Governance requires traceable evidence

Cost risk

low

Delivery complexity

medium

Why not prompt-only

· Prompt-only answers lack grounding and citations

Why not fine-tune

· Fine-tuning can memorize outdated guidance; source freshness matters more
· Higher governance burden and harder rollback

Approach	Best when	Key risks
Prompting	Simple, instruction-following, low risk	Brittle prompts, weak grounding
RAGrecommended	Current documents, citations, changing knowledge	Stale sources, weak retrieval
Fine-tuning	Consistent behavior/format, stable task, labeled data	Overfitting, leakage, harder rollback
Traditional ML	Structured prediction/classification with labels	Bias, imbalance, poor generalization
Hybrid	Workflow needs evidence + responses + actions	Complex debugging, integration risk

Training dataset readiness

Training dataset not required

not-required

Training dataset not required — this initiative is better served by RAG, evaluation datasets, and monitoring, because answers must stay grounded in current source documents.

Recommended: Training dataset not required — this initiative is better served by RAG, evaluation datasets, and monitoring, because answers must stay grounded in current source documents.

Data hygiene

Train / validation / test split

Train teaches the model, validation tunes decisions, test estimates unseen performance. If examples leak across sets, the model looks strong in testing but fails in the real world.

No split required — RAG/eval datasets are used instead of a supervised training split.

Model quality

Overfitting & generalization

Overfitting = strong on seen examples, weak on new cases. Generalization = performing well on unseen examples. Detect overfitting before release.

Train vs validation accuracy — where memorizing begins

medium overfitting risk · 12.9pt gap

Labeled dataset size2,400 examples

Validation plateaus near epoch 5 while training keeps climbing — a 12.9pt gap. Usable with early stopping and a clean holdout.

Simulated curves — no model is trained. The shape is deterministic and driven by dataset size.

Scenario	Train	Validation	Test	Risk
Healthy generalization	91%	88%	87%	low
Overfitting risk	98%	74%	71%	high
Underfitting risk	62%	60%	59%	medium

Training readiness contract

What flows to Operate & Govern

Enabled

No (RAG preferred)

Approach

rag

Generalization

n/a

Overfitting risk

low

→ Operate (monitoring)

· Answer-quality drift monitoring

→ Govern (controls)

· Prefer RAG with citation evidence; no fine-tune governance burden required yet

For reviewers

What this training layer demonstrates

This layer demonstrates that enterprise AI teams should not fine-tune by default. The right approach depends on the workflow, data, freshness, governance burden, and evaluation evidence. Fine-tuning requires high-quality labeled data, clean splits, holdout evaluation, overfitting checks, and operational monitoring.

Fine-tune vs RAG decisioning

A memo picks the right approach from the initiative, not a default.

Labeled-data readiness

Quality, consistency, balance, and coverage are checked before training.

Train/validation/test split

Leakage and holdout gaps are surfaced explicitly.

Overfitting & generalization

Train-vs-test gaps and risk triggers are made visible.

Evaluation & monitoring

Holdout eval + drift + class-level monitoring requirements.

Governance handoff

Training risk becomes controls and findings in Govern.

Training simulation boundary

This portfolio demo does not train or fine-tune a model. It uses deterministic readiness checks and simulated learning-curve examples to show the decisions enterprise teams make before training. In production these contracts could connect to labeling platforms, model registries, eval stores, MLflow, W&B, SageMaker, Vertex AI, or Azure ML. No real training is performed here.