ai-engineering-patterns

To implement agentic RAG for document-aware responses, use this Skill's patterns to combine semantic search via embedding models with LLM-powered analysis. It enables retrieval-augmented generation for tactical marketplace scenarios like product search.

Context engineering for long-running agent sessions involves managing knowledge persistence and contextual data over time. This Skill provides patterns to maintain continuous agent state, ensuring coherent responses during complex investigation tasks.

You coordinate multi-agent systems for deep research by decomposing complex queries into parallel investigation tasks. This Skill provides patterns to orchestrate multiple agents, enabling them to synthesize findings into a unified result.

Yes, you can use LLMs for financial analysis and market trend forecasting. This Skill offers patterns leveraging LLM-powered analysis to process market data, identify trends, and generate revenue forecasts for tactical marketplace scenarios.

MCP infrastructure connects AI agents to diverse data sources and external tools. This Skill provides patterns to establish these connections, allowing your agents to retrieve and process information from multiple platforms during complex investigations.

Yes, you need a solid understanding of embedding models and hybrid search to apply these AI patterns effectively. The Skill requires prerequisite knowledge of semantic search, LLM-powered analysis, and agent coordination techniques.