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Amazon Bedrock Automated Reasoning for mathematical verification of AI responses against formal policy rules with up to 99% accuracy. Use when validating healthcare protocols, financial compliance, legal regulations, insurance policies, or any domain requiring deterministic verification of AI-generated content.

Amazon Bedrock Automated Reasoning for mathematical verification of AI responses against formal policy rules with up to 99% accuracy. Use when validating healthcare protocols, financial compliance, legal regulations, insurance policies, or any domain requiring deterministic verification of AI-generated content.

Amazon Bedrock Model Customization with fine-tuning, continued pre-training, reinforcement fine-tuning (NEW 2025 - 66% accuracy gains), and distillation. Create customization jobs, monitor training, deploy custom models, and evaluate performance. Use when customizing Claude, Titan, or other Bedrock models for domain-specific tasks, adapting to proprietary data, improving accuracy on specialized workflows, or distilling large models to smaller ones.

Amazon Bedrock Model Customization with fine-tuning, continued pre-training, reinforcement fine-tuning (NEW 2025 - 66% accuracy gains), and distillation. Create customization jobs, monitor training, deploy custom models, and evaluate performance. Use when customizing Claude, Titan, or other Bedrock models for domain-specific tasks, adapting to proprietary data, improving accuracy on specialized workflows, or distilling large models to smaller ones.

Build visual AI workflows with Amazon Bedrock Flows. Create flows with prompt nodes, knowledge bases, Lambda, inline code, condition branching, iterators, collectors, and DoWhile loops. Version management, aliases, deployment. Use when building multi-step AI workflows, orchestrating models and services, creating condition-based routing, implementing iterative processing, or deploying production AI pipelines.

Build visual AI workflows with Amazon Bedrock Flows. Create flows with prompt nodes, knowledge bases, Lambda, inline code, condition branching, iterators, collectors, and DoWhile loops. Version management, aliases, deployment. Use when building multi-step AI workflows, orchestrating models and services, creating condition-based routing, implementing iterative processing, or deploying production AI pipelines.

Comprehensive Amazon Bedrock Guardrails implementation for AI safety with 6 safeguard policies (content filters, PII redaction, topic denial, word filters, contextual grounding, automated reasoning). Use when implementing content moderation, detecting prompt attacks, preventing hallucinations, protecting sensitive data, enforcing compliance policies, or securing generative AI applications with mathematical verification.

Comprehensive Amazon Bedrock Guardrails implementation for AI safety with 6 safeguard policies (content filters, PII redaction, topic denial, word filters, contextual grounding, automated reasoning). Use when implementing content moderation, detecting prompt attacks, preventing hallucinations, protecting sensitive data, enforcing compliance policies, or securing generative AI applications with mathematical verification.

Amazon Bedrock Runtime API for model inference including Claude, Nova, Titan, and third-party models. Covers invoke-model, converse API, streaming responses, token counting, async invocation, and guardrails. Use when invoking foundation models, building conversational AI, streaming model responses, optimizing token usage, or implementing runtime guardrails.

Amazon Bedrock Runtime API for model inference including Claude, Nova, Titan, and third-party models. Covers invoke-model, converse API, streaming responses, token counting, async invocation, and guardrails. Use when invoking foundation models, building conversational AI, streaming model responses, optimizing token usage, or implementing runtime guardrails.

Amazon Bedrock Knowledge Bases for RAG (Retrieval-Augmented Generation). Create knowledge bases with vector stores, ingest data from S3/web/Confluence/SharePoint, configure chunking strategies, query with retrieve and generate APIs, manage sessions. Use when building RAG applications, implementing semantic search, creating document Q&A systems, integrating knowledge bases with agents, optimizing chunking for accuracy, or querying enterprise knowledge.

Amazon Bedrock Knowledge Bases for RAG (Retrieval-Augmented Generation). Create knowledge bases with vector stores, ingest data from S3/web/Confluence/SharePoint, configure chunking strategies, query with retrieve and generate APIs, manage sessions. Use when building RAG applications, implementing semantic search, creating document Q&A systems, integrating knowledge bases with agents, optimizing chunking for accuracy, or querying enterprise knowledge.

Amazon Bedrock Prompt Management for creating, versioning, and managing prompt templates with variables, multi-variant A/B testing, and flow integration. Use when creating reusable prompt templates, managing prompt versions, implementing A/B testing for prompts, integrating prompts with Bedrock Flows, optimizing prompt engineering, or building production prompt catalogs.

Amazon Bedrock Prompt Management for creating, versioning, and managing prompt templates with variables, multi-variant A/B testing, and flow integration. Use when creating reusable prompt templates, managing prompt versions, implementing A/B testing for prompts, integrating prompts with Bedrock Flows, optimizing prompt engineering, or building production prompt catalogs.

Extract learnings and best practices from skill development experience, review findings, and pattern analysis. Task-based operations for pattern extraction, learning documentation, guideline updates, knowledge sharing, and continuous improvement. Use when extracting learnings from completed skills, updating best practices, improving development process, or feeding continuous improvement cycle.

Extract learnings and best practices from skill development experience, review findings, and pattern analysis. Task-based operations for pattern extraction, learning documentation, guideline updates, knowledge sharing, and continuous improvement. Use when extracting learnings from completed skills, updating best practices, improving development process, or feeding continuous improvement cycle.

Complete design system guide for Dr. Sophia AI's botanical brand identity. Includes official typography (Livvic/Onest), color palette (#00B368 green spectrum), shadow systems, gradients, and component templates. Use when creating or modifying UI components, implementing brand colors, or ensuring design consistency across the application.

AWS Boto3 SDK patterns for Amazon ECS cluster management, task definitions, services, and Fargate deployments. Use when working with ECS clusters, managing task definitions, deploying services, running one-off tasks, monitoring deployments, or integrating ECS with Python applications.

AWS Boto3 SDK patterns for Amazon ECS cluster management, task definitions, services, and Fargate deployments. Use when working with ECS clusters, managing task definitions, deploying services, running one-off tasks, monitoring deployments, or integrating ECS with Python applications.

AWS Boto3 SDK patterns for Amazon EKS cluster management, node groups, authentication tokens, and Kubernetes client integration. Use when working with EKS clusters, managing node groups, generating kubeconfig, creating authentication tokens, integrating Kubernetes Python client, managing Fargate profiles, or implementing IRSA authentication.