Content Domain 4: Operational Efficiency and Optimization for GenAI Applications

Skill 4.1.1: Develop token efficiency systems to reduce FM costs while maintaining effectiveness (for example, by using token estimation and tracking, context window optimization, response size controls, prompt compression, context pruning, response limiting).

Skill 4.1.2: Create cost-effective model selection frameworks (for example, by using cost-capability tradeoff evaluation, tiered FM usage based on query complexity, inference cost balancing against response quality, price-to-performance ratio measurement, efficient inference patterns).

Skill 4.1.3: Develop high-performance FM systems to maximize resource utilization and throughput for GenAI workloads (for example, by using batching strategies, capacity planning, utilization monitoring, auto-scaling configurations, provisioned throughput optimization).

Skill 4.1.4: Create intelligent caching systems to reduce costs and improve response times by avoiding unnecessary FM invocations (for example, by using semantic caching, result fingerprinting, edge caching, deterministic request hashing, prompt caching).

Skill 4.2.1: Create responsive AI systems to address latency-cost tradeoffs and improve the user experience with FMs (for example, by using pre-computation to perform predictable queries, latency-optimized Amazon Bedrock models for time-sensitive applications, parallel requests for complex workflows, response streaming, performance benchmarking).

Skill 4.2.2: Enhance retrieval performance to improve the relevance and speed of retrieved information for FM context augmentation (for example, by using index optimization, query preprocessing, hybrid search implementation with custom scoring).

Skill 4.2.3: Implement FM throughput optimization to address the specific throughput challenges of GenAI workloads (for example, by using token processing optimization, batch inference strategies, concurrent model invocation management).

Skill 4.2.4: Enhance FM performance to achieve optimal results for specific GenAI use cases (for example, by using model-specific parameter configurations, A/B testing to evaluate improvements, appropriate temperature and top-k/top-p selection based on requirements).

Skill 4.2.5: Create efficient resource allocation systems specifically for FM workloads (for example, by using capacity planning for token processing requirements, utilization monitoring for prompt and completion patterns, auto-scaling configurations that are optimized for GenAI traffic patterns).

Skill 4.2.6: Optimize FM system performance for GenAI workflows (for example, by using API call profiling for prompt-completion patterns, vector database query optimization for retrieval augmentation, latency reduction techniques specific to LLM inference, efficient service communication patterns).

Skill 4.3.1: Create holistic observability systems to provide complete visibility into FM application performance (for example, by using operational metrics, performance tracing, FM interaction tracing, business impact metrics with custom dashboards).

Skill 4.3.2: Implement comprehensive GenAI monitoring systems to proactively identify issues and evaluate key performance indicators specific to FM implementations (for example, by using CloudWatch to track token usage; prompt effectiveness; hallucination rates; and response quality, anomaly detection for token burst patterns and response drift, Amazon Bedrock Model Invocation Logs to perform detailed request and response analysis, performance benchmarks, cost anomaly detection).

Skill 4.3.3: Develop integrated observability solutions to provide actionable insights for FM applications (for example, by using operational metric dashboards, business impact visualizations, compliance monitoring, forensic traceability and audit logging, user interaction tracking, model behavior pattern tracking).

Skill 4.3.4: Create tool performance frameworks to ensure optimal tool operation and utilization for FMs (for example, by using call pattern tracking, performance metric collection, tool calling observability and multi-agent coordination tracking, usage baselines for anomaly detection).

Skill 4.3.5: Create vector store operational management systems to ensure optimal vector store operation and reliability for FM augmentation (for example, by using performance monitoring for vector databases, automated index optimization routines, data quality validation processes).

Skill 4.3.6: Develop FM-specific troubleshooting frameworks to identify unique GenAI failure modes that are not present in traditional ML systems (for example, by using golden datasets to detect hallucinations, output diffing techniques to conduct response consistency analysis, reasoning path tracing to identify logical errors, specialized observability pipelines).