docs(bedrock): Bedrock fine-tuning reference + PT/Reserved Tier guidance

.gitignore

@@ -8,6 +8,9 @@ Thumbs.db
 *.swp
 *.swo
 
+# Trunk (linter — local cache/symlinks, do not track)
+.trunk/
+
 # Python (uvx/MCP servers)
 __pycache__/
 *.pyc

plugins/aws-dev-toolkit/skills/bedrock/SKILL.md

@@ -1,6 +1,6 @@
 ---
 name: bedrock
-description: Deep-dive into Amazon Bedrock — model selection, agents, knowledge bases, guardrails, prompt engineering, and cost modeling. This skill should be used when the user asks to "build with Bedrock", "select a Bedrock model", "design a Bedrock agent", "set up a knowledge base", "configure guardrails", "estimate Bedrock costs", "optimize Bedrock pricing", "use prompt caching", "compare Bedrock models", or mentions Amazon Bedrock, foundation models, RAG on AWS, or generative AI on AWS.
+description: Deep-dive into Amazon Bedrock — model selection, agents, knowledge bases, guardrails, prompt engineering, model customization (fine-tuning), and cost modeling. This skill should be used when the user asks to "build with Bedrock", "select a Bedrock model", "design a Bedrock agent", "set up a knowledge base", "configure guardrails", "estimate Bedrock costs", "optimize Bedrock pricing", "use prompt caching", "compare Bedrock models", "fine-tune a model", "customize a model", "distill a model", "import a custom model", or mentions Amazon Bedrock, foundation models, RAG on AWS, model customization, fine-tuning, distillation, or generative AI on AWS.
 ---
 
 Specialist guidance for Amazon Bedrock. Covers model selection, agent design, knowledge bases, guardrails, prompt engineering, batch inference, and cost optimization.
@@ -92,6 +92,14 @@ The model choice is the single biggest cost and quality decision. Get this right
 - Request JSON with explicit schemas to reduce output token waste
 - Use the Converse API with tool use for structured extraction — more reliable than asking for JSON in the prompt
 
+## Model Customization (Fine-Tuning)
+
+Fine-tuning is rarely the first move — rule out better prompting, RAG/Knowledge Bases, and prompt caching first. FT changes *behavior/style*, not *knowledge*; for facts, use RAG. Fine-tune only when the task is narrow, repeated at volume, has a stable target, and prompting has plateaued.
+
+When a customization task comes up — choosing a method (SFT, distillation, DPO, RFT, CPT), picking a fine-tuning target, the train→deploy→bill lifecycle, evaluation, or the many operational gotchas (region availability, console "deprecated model" errors, native FT vs Custom Model Import vs Bring Your Own Model, Provisioned Throughput vs Reserved Tier (prefer RT where available, but it's gated by per-model availability), `-1` loss fields, escrow-account artifact opacity) — **load `references/fine-tuning.md`**. It is the detailed, field-tested guide and the single source of truth for this topic.
+
+**Always verify against current docs first.** Customization is the fastest-moving surface in Bedrock — method × model support, fine-tunable base models, regions, and PT/Reserved Tier availability all change frequently. Before committing to any customization design, confirm via the `awsknowledge` MCP tools (`mcp__plugin_aws-dev-toolkit_awsknowledge__aws___search_documentation`, `mcp__plugin_aws-dev-toolkit_awsknowledge__aws___read_documentation`, `mcp__plugin_aws-dev-toolkit_awsknowledge__aws___recommend`).
+
 ## Batch Inference
 - 50% cheaper than on-demand for supported models
 - Use for: document processing, bulk classification, dataset enrichment, eval runs
@@ -146,8 +154,9 @@ aws bedrock get-model-invocation-logging-configuration
 
 ### Reference Files
 
-For detailed cost modeling and estimation, consult:
+For detailed guidance, consult:
 - **`references/cost-modeling.md`** — Pricing model breakdown, cost modeling template, optimization strategies, monitoring setup, and cost estimation output format
+- **`references/fine-tuning.md`** — Model customization: deciding whether to fine-tune, picking the method (SFT/distillation/DPO/RFT/CPT), choosing a target, the distillation pattern, operational gotchas, cost model, evaluation (fidelity vs accuracy), end-to-end API flow, and anti-patterns
 
 ### Related Skills
 - **`cost-check`** — Broader AWS cost analysis beyond Bedrock

plugins/aws-dev-toolkit/skills/bedrock/references/cost-modeling.md

@@ -7,7 +7,8 @@ Bedrock charges per token (input and output separately). Key variables:
 - **Output tokens**: Model's response. Control via max_tokens and prompt design.
 - **Cached input tokens**: Repeated system prompts cached by Bedrock — up to 90% cheaper for supported models.
 - **Batch inference**: 50% discount for async, non-real-time workloads.
-- **Provisioned throughput**: Committed capacity — only for high, sustained volume. Minimum commitment is 1 month.
+- **Provisioned throughput (PT)**: Committed capacity on the traditional Bedrock infrastructure — only for high, sustained volume. Minimum commitment is 1 month.
+- **Reserved Tier**: Committed capacity on the Mantle infrastructure — the capacity-reservation lever for models served there, and the preferable option where a model offers it. PT and Reserved Tier are usually mutually exclusive per model (a model is on one infrastructure or the other), so lean toward Reserved Tier when available and fall back to PT when it isn't. Availability is per-model — confirm support for your target model in current AWS docs before modeling cost around it.
 
 ## Cost Modeling Template