# Fine-tuning

> YogoQ Core AI-readable term handoff. Preview, read-only, Reviewed/Verified only.

- Canonical URL: https://core.yogoq.com/en-US/core/fine-tuning
- Locale: en-US
- Content tier: db_backed
- Quality: reviewed
- Publication status: published_reviewed
- Schema version: core-reviewed-term-ai-handoff-v2
- Compatible with: core-reviewed-term-ai-handoff-v1
- Content hash: 57dfd1d8f58ea19687a9bbaf22637e0bc5a6166003ee969122ec7a55a4828823
- Trust policy: core-trust-policy-v1-2026-06-22

## Short Definition

Fine-tuning adapts an existing model with additional training data so it better follows a domain, format, style, or classification policy. It should be compared against prompting and retrieval before adoption.

## 一言でいうと

Fine-tuning adapts an existing model with additional training data so it better follows a domain, format, style, or classification policy. It should be compared against prompting and retrieval before adoption.

## 計算の考え方

Evaluate fine-tuning by improvement over a baseline and by ongoing operating cost. Quality lift | Tuned score - baseline score | Shows whether training adds value Consistency lift | Reduction in format or policy violations | Shows stability gains Total cost | Training + evaluation + operations - prompt savings | Tests long-term viability

- Quality lift | Tuned score - baseline score | Shows whether training adds value
- Consistency lift | Reduction in format or policy violations | Shows stability gains
- Total cost | Training + evaluation + operations - prompt savings | Tests long-term viability

## 含めるもの / 含めないもの

Fine-tuning adapts behavior. It is not a substitute for fresh knowledge retrieval or authorization. Include | Style, format, classification policy, domain phrasing, stable repeated tasks | Good candidates for tuning Exclude | Fresh facts, database access, permissions, truth guarantees | Use retrieval or tools Make explicit | Data source, eval set, failure conditions, retraining triggers | Keeps quality auditable

- Include | Style, format, classification policy, domain phrasing, stable repeated tasks | Good candidates for tuning
- Exclude | Fresh facts, database access, permissions, truth guarantees | Use retrieval or tools
- Make explicit | Data source, eval set, failure conditions, retraining triggers | Keeps quality auditable

## 意味

Fine-tuning adjusts a pretrained model with additional examples chosen for a specific task. It can improve consistent formatting, domain language, classification behavior, tone, or repeated workflow behavior. It is usually not the right tool for keeping current facts, enforcing permissions, or reading private databases at request time; retrieval and tool use often fit those needs better. A responsible fine-tuning decision requires data provenance, privacy review, bias review, baseline comparison, holdout evaluation, and rollback planning.

## 役立つ場面

Teams can decide whether a consistency problem should be solved through training rather than prompts. Fresh-knowledge needs can be routed to retrieval or tools instead of being forced into training. Separating training and evaluation data reduces the risk of overestimating performance.

- Teams can decide whether a consistency problem should be solved through training rather than prompts.
- Fresh-knowledge needs can be routed to retrieval or tools instead of being forced into training.
- Separating training and evaluation data reduces the risk of overestimating performance.

## 使い方のポイント

- Fine-tuning adapts behavior; it is not a universal knowledge update mechanism.
- Try prompting, retrieval, and tool use before training when possible.
- Data quality, rights, confidentiality, and bias directly affect the tuned model.
- A baseline and holdout evaluation are required to judge success.
- Production use needs monitoring, retraining criteria, and rollback.

## 何が数字を動かすか

Impact depends on data quality, evaluation design, baseline strength, and task stability. Data quality | Clean, consistent examples improve behavior Evaluation | Holdout tests prevent false confidence Task stability | Changing requirements increase retraining cost Baseline | If prompting or RAG is enough, tuning may be unnecessary

- Data quality | Clean, consistent examples improve behavior
- Evaluation | Holdout tests prevent false confidence
- Task stability | Changing requirements increase retraining cost
- Baseline | If prompting or RAG is enough, tuning may be unnecessary

## 判断するときの注意点

Bad training data can make bad behavior more consistent. Do not mix training and evaluation data; overfitting hides production failures. Do not use confidential or rights-unclear data without approval. Re-evaluate when the base model, product policy, or data distribution changes.

- Do not mix training and evaluation data; overfitting hides production failures.
- Do not use confidential or rights-unclear data without approval.
- Re-evaluate when the base model, product policy, or data distribution changes.

## よくある誤解 / 落とし穴

- Fine-tuning should not be used to memorize all internal knowledge. Fresh and permissioned facts need retrieval or tools.
- More data is not always better. Inconsistent examples can reduce quality.
- A tuned model still requires evaluation and monitoring.

## 最小例

A customer success team wants consistent classification of churn reasons into ten labels. Prompting produces inconsistent labels for similar tickets. The team considers fine-tuning with previously verified labeled tickets, removes personal data, and keeps a separate holdout set. The tuned model improves classification consistency, but it still misclassifies reasons tied to a new pricing plan. The team keeps plan information in retrieval and limits fine-tuning to stable label behavior. The launch decision is based on holdout accuracy, review effort, and a rollback path to the prompt-only baseline.

## 似ている言葉との違い

Fine-tuning | Adapts behavior through training | Useful for consistency Prompting | Controls each request | Useful when requirements change often RAG | Retrieves knowledge | Useful for current facts and evidence

- Fine-tuning | Adapts behavior through training | Useful for consistency
- Prompting | Controls each request | Useful when requirements change often
- RAG | Retrieves knowledge | Useful for current facts and evidence

## 一緒に見る指標

Fine-tuning should be selected against prompting, retrieval, and evaluation alternatives. Prompt Engineering | Improves behavior through input design | Usually the first layer to try RAG | Supplies external knowledge at request time | Better for freshness and citations AI Evaluation | Measures before and after | Required for launch decisions

- Prompt Engineering | Improves behavior through input design | Usually the first layer to try
- RAG | Supplies external knowledge at request time | Better for freshness and citations
- AI Evaluation | Measures before and after | Required for launch decisions

## Aliases

- Fine-tuning (display_name, en-US)
- ファイン・チューニング (katakana, en-US)
- Fine-tuning (english_name, en-US)
- ファインチューニング (localized_title, ja-JP)

## Relations

- Generative AI: related (https://core.yogoq.com/en-US/core/generative-ai)
- AI Evaluation: related (https://core.yogoq.com/en-US/core/ai-evaluation)
- Prompt Engineering: compare (https://core.yogoq.com/en-US/core/prompt-engineering)

## RAG Chunks

- core:chunk:fine-tuning:en-US:definition:f90dd5debab04841
- core:chunk:fine-tuning:en-US:formula:9b1963ead443c389
- core:chunk:fine-tuning:en-US:boundary:e719e942d770d141
- core:chunk:fine-tuning:en-US:meaning:81844c3aec13ddc6
- core:chunk:fine-tuning:en-US:usage:b5bba5d318503568
- core:chunk:fine-tuning:en-US:usage:5c43f43896c30f21
- core:chunk:fine-tuning:en-US:drivers:88a8dca0c5db3a70
- core:chunk:fine-tuning:en-US:misunderstandings:321cb9195d11df1d
- core:chunk:fine-tuning:en-US:misunderstandings:a6eef1e998d8cab8
- core:chunk:fine-tuning:en-US:examples:b06339fabaa4390b
- core:chunk:fine-tuning:en-US:comparisons:65b55c41eac95718
- core:chunk:fine-tuning:en-US:related_metrics:bd0c5d67b68ba162
- core:chunk:fine-tuning:en-US:faq:7c3d47cccb20fef6
- core:chunk:fine-tuning:en-US:faq:d219e436220ed3cc
- core:chunk:fine-tuning:en-US:faq:67438ae1e9ed9cab

## FAQ

### Does fine-tuning replace RAG?

Usually no. Use RAG or tools for fresh, source-grounded, or permissioned information.

### When is it worth trying?

When prompts do not make a stable behavior reliable and you have clean training data plus a holdout evaluation set.

### What is the main risk?

Training on noisy, confidential, biased, or rights-unclear data can make the wrong behavior consistent.

## Sources

- NIST: Generative AI Profile - https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf
- NIST: AI RMF - https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf

## Limitations

This page is reference information for research and learning. For accounting, legal, finance, health, security, or other individual decisions, confirm against primary sources or qualified professionals.

- Public pages support general understanding and practical context; they are not professional advice for individual cases.
- Fast-changing information such as regulations, accounting standards, prices, product specs, and legal requirements should be checked against primary sources before final decisions.
- Even when AI-assisted drafting or audit is used, publication relies on quality gates and human-readable evidence.