bedrock

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.

Specialist guidance for Amazon Bedrock. Covers model selection, agent design, knowledge bases, guardrails, prompt engineering, batch inference, and cost optimization.

Process

1. Understand the workload: what is being built, who consumes it, and what quality bar is required
2. Use 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) to verify current Bedrock model availability, pricing, and features (these change frequently)
3. Select the right model(s) based on task complexity, latency, and cost
4. Design the architecture: direct invocation, RAG, agent, or multi-agent
5. Configure guardrails for user-facing surfaces
6. Estimate costs using the references/cost-modeling.md template
7. Recommend monitoring and cost controls

Model Selection

The model choice is the single biggest cost and quality decision. Get this right first.

Need	Recommended Model	Why
Classification, routing, extraction	Nova Micro or Claude Haiku	Fast, cheap, accurate for structured tasks
General Q&A, summarization	Nova Lite or Nova Pro	Strong quality-to-cost ratio
Multimodal (image + text)	Nova Lite	Cost-effective vision without Sonnet pricing
Complex reasoning, nuanced generation	Claude Sonnet	Best balance of capability and cost
Hardest problems, highest quality bar	Claude Opus	Reserve for tasks where Sonnet falls short
Embeddings	Titan Embed v2	Cheaper than Cohere, solid quality for most use cases
Code generation	Claude Sonnet	Strong code quality without Opus pricing

Note: Model availability and pricing change frequently. Verify current options via 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) before making final recommendations.

Model Selection Principles

• Start with the smallest model that could work. Upgrade only when evidence shows it falls short.
• Benchmark on real data, not generic benchmarks. A smaller well-prompted model often beats a larger general one.
• Use Bedrock's intelligent prompt routing to auto-route requests to the right model tier.
• Evaluate the Nova family before defaulting to third-party models — Nova Pro offers comparable quality to Claude Sonnet for many tasks at significantly lower cost per token, and Nova Lite/Micro provide sub-100ms latency for classification and routing tasks where you don't need full reasoning capability. Nova models also have no cross-provider data transfer fees and deeper native Bedrock integration (Guardrails, Knowledge Bases, Flows).

Bedrock Agents

Design Principles

• One agent, one job. If the agent description contains "and", consider splitting.
• Fewer tools = fewer reasoning steps = faster + cheaper. 3-5 tools is the sweet spot.
• Use direct InvokeModel for simple tasks. Not everything needs an agent.

Architecture Patterns

Router + Specialists: A lightweight classifier (Nova Micro) routes to specialized agents. Each specialist has a focused tool set and optimized prompt. This beats one mega-agent with 20 tools.

Knowledge Base + Guardrails: For customer-facing Q&A — KB for retrieval, guardrails for safety, single model call for generation. No agent orchestration needed; use RetrieveAndGenerate API directly.

Agent with Session Memory: For multi-turn conversations — use AgentCore sessions with memory. Let the agent maintain context across turns instead of stuffing history into the prompt each time.

Action Groups

• Use Lambda-backed action groups for complex logic
• Use Return Control for client-side tool execution (keeps agent stateless, avoids Lambda cost)
• Define OpenAPI schemas tightly — vague schemas cause the model to guess (and guess wrong)

Knowledge Bases

Chunking Strategy

• Fixed-size chunking (default): Good starting point. 300-500 tokens with 10-20% overlap.
• Semantic chunking: Better quality, higher embedding cost. Use for high-value, heterogeneous documents.
• Hierarchical chunking: Best for long documents with clear structure (manuals, legal docs).
• Curate the data source — garbage in, garbage out applies doubly to RAG.

Vector Store Selection

• OpenSearch Serverless: Default choice. Managed, scales, integrates natively. See references/cost-modeling.md for minimum costs.
• Aurora PostgreSQL (pgvector): Good if already running Aurora — consolidates infrastructure.
• Pinecone / Redis: If existing investments in these stores.
• For PoCs, share a single OpenSearch Serverless collection across multiple KBs to minimize cost.

Retrieval Tuning

• Start with hybrid search (semantic + keyword) — outperforms pure semantic for most workloads
• Tune retrieved chunk count (default 5). More chunks = more context = more input tokens. Find the minimum that gives good answers.
• Use metadata filtering to scope retrieval — avoid searching everything when the document category is known.

Prompt Engineering on Bedrock

Prompt Caching

• Bedrock caches repeated system prompts automatically for supported models
• Structure prompts: long, stable system prompt + short, variable user prompt
• Cached input tokens are up to 90% cheaper — structure prompts to maximize cache hits

Prompt Management

• Use Bedrock's Prompt Management to version and manage prompts
• Treat prompts like code — version them, test them, review changes
• Use prompt variables for dynamic content instead of string concatenation

Structured Output

• 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

Batch Inference

• 50% cheaper than on-demand for supported models
• Use for: document processing, bulk classification, dataset enrichment, eval runs
• Not for: real-time user-facing requests (latency is minutes to hours)
• Submit jobs via S3 input/output — fits naturally into data pipelines

Guardrails

• Apply to user-facing inputs and outputs. Skip for internal agent reasoning steps.
• Content filters are cheaper than denied topic policies — use filters for broad categories, denied topics for specific restrictions.
• Contextual grounding checks catch hallucination at inference time — useful for RAG apps.
• PII detection/redaction is built in — use it instead of building custom regex.

Diagnostic CLI Commands

Resource creation belongs in IaC. Use the iac-scaffold skill for templates.

# List available models in the region
aws bedrock list-foundation-models \
  --query 'modelSummaries[].{id:modelId,name:modelName,provider:providerName}' --output table

# Quick model test (Converse API — preferred over invoke-model)
aws bedrock-runtime converse \
  --model-id amazon.nova-micro-v1:0 \
  --messages '[{"role":"user","content":[{"text":"Hello"}]}]'

# List agents
aws bedrock-agent list-agents --output table

# List knowledge bases
aws bedrock-agent list-knowledge-bases --output table

# List guardrails
aws bedrock list-guardrails --output table

# Check model invocation logging status
aws bedrock get-model-invocation-logging-configuration

Anti-Patterns

• Defaulting to the biggest model "just to be safe" — start small, upgrade with evidence
• Building an agent when a single InvokeModel call would do — agents compound cost per turn
• Stuffing entire documents into prompts instead of using Knowledge Bases — RAG is cheaper and more maintainable
• Ignoring prompt caching — it is automatic for supported models, just structure prompts correctly
• Using on-demand for bulk processing that could be batch — 50% savings left on the table
• One massive Knowledge Base instead of scoped, curated collections — hurts retrieval quality and costs more
• Skipping guardrails on user-facing apps — "we'll add them later" becomes a security incident
• Not monitoring token usage — costs sneak up fast during iteration, especially with agents

Additional Resources

Reference Files

For detailed cost modeling and estimation, consult:

• references/cost-modeling.md — Pricing model breakdown, cost modeling template, optimization strategies, monitoring setup, and cost estimation output format

Related Skills

• cost-check — Broader AWS cost analysis beyond Bedrock
• iac-scaffold — IaC templates for Bedrock resource creation
• security-review — Security audit for Bedrock configurations and guardrail policies

Output Format

When advising on a Bedrock solution:

Component	Choice	Rationale
Primary model	Claude Sonnet	Complex reasoning required, cost-effective for the quality bar
Routing model	Nova Micro	Cheap classifier for request triage
Architecture	Router + Specialist agents	3 focused agents vs 1 mega-agent
Knowledge Base	OpenSearch Serverless, hybrid search	Best retrieval quality, managed infrastructure
Guardrails	Content filters + PII redaction	Customer-facing surface
Estimated monthly cost	$X,XXX	See references/cost-modeling.md for breakdown

Include cost profile and watch-out-for items specific to the use case.