--- title: "Harnessing Artificial Intelligence for Innovative Solutions" description: "Aleksandr Filippov leverages cutting-edge AI frameworks and methodologies to create robust and innovative solutions, enhancing organizational capabilities and decision-making processes." date: "2024-03-17T07:00:00Z" last_updated: "2026-08-28" build_time: "2026-08-31T12:31:21Z" skill_name: "Artificial Intelligence" skill_description: "LLM engineering across retrieval-augmented generation with vector and hybrid search, agent and retrieval orchestration on LangChain and LangGraph, Model Context Protocol tooling, LangSmith observability, and evaluation-driven delivery of production AI systems." skill_category: ["ai-platforms-agents","ai-engineering"] since: "2023-05-01" last_used: "2026-07-27" related_skills: ["product-management","stakeholder-management","business-analysis","system-analysis","product-technology-leadership","python","claude-code","mcp"] related_projects: ["aegis","mcp-context-server","claude-code-toolbox","claude-code-artifacts","air-api","aila","youtrack-mcp-server","agile-coach-pro","single-platform-for-retailers-and-buyers","ml-prep-and-train-toolkit","cxr-draft-auditor"] related_experience: ["ai-product-manager-at-spotware"] certifications: ["build-small-hackathon-2026-by-hugging-face","career-essentials-in-generative-ai-by-microsoft"] keywords: ["Artificial Intelligence","Large Language Models","LLM Engineering","Retrieval-Augmented Generation","RAG","Model Context Protocol","MCP","Agentic Workflows","Multi-Agent Orchestration","AI Evaluation","LangChain","LangGraph","LangSmith","Prompt Engineering","Structured Output","Tool Calling","Vector Search","Embeddings","Semantic Search","Hybrid Search","Cross-Encoder Reranking","Human-in-the-Loop","LLM Observability","Model Evaluation","Fine-Tuning","Python"] license: "https://creativecommons.org/licenses/by/4.0/" canonical: "https://www.alexfeel.info/skills/artificial-intelligence/" --- My artificial-intelligence skill is organized around **modern large language models and the engineering discipline that turns them into production systems**. I work fluently across the current LLM surface — multiple model families and providers (OpenAI and Azure OpenAI, Anthropic, Google, and open-weight models served locally through Ollama), prompt engineering for production use, structured output validated against Pydantic schemas, streaming and tool-calling patterns, token and context-window budgeting, and the quality and cost trade-offs that decide whether an LLM idea ever reaches real users. On top of that base I practice **retrieval-augmented generation, agentic orchestration, and evaluation as first-class disciplines**. In retrieval that means chunking, embeddings, vector search, full-text search, hybrid ranking with reciprocal rank fusion, cross-encoder reranking, query decomposition and rewrites, two-layer storage for semantic matching against parent documents, and multilingual retrieval patterns. In agents it means multi-agent orchestration with explicit specialization and handoff, context engineering and short-term / long-term memory design, tool-calling through the **Model Context Protocol (MCP)** for grounded access to knowledge bases and systems of record, and the discipline of validating agent output against verifiable sources. The orchestration layer I build on is **LangChain and LangGraph**: in production that is a compiled state graph with conditional routing, where a query is language-detected, decomposed, rewritten, retrieved against, reranked, and resolved as separate nodes rather than one opaque prompt; in my own agent work it extends to checkpointed state across turns, middleware for conversation summarization and task planning, and a human-in-the-loop interrupt-and-resume path — built and tested, ready for the first tool that should require a person's approval before it acts; that research is what led me into the corporate agent platform I am building now, a shared runtime early in its development where any team's agents are stored configurations rather than separate codebases. I treat evaluation and operability as product requirements, concretely rather than aspirationally: **LangSmith tracing** wired into the production retrieval service so latency, token usage, and cost are observed instead of guessed; a blind, scored quality comparison against professional human output before the system went wide; a held-out evaluation harness that reports precision, recall, and F1 against ground truth; and retry ladders, embedding-provider fallback, and visible graceful degradation, so a bad generation is a logged and recoverable event rather than a silent one. LLM systems that are not measured drift silently. I apply this skill with a **platform-first posture** — build reusable primitives (retrieval, agent tooling, memory, access models) once and compound multiple products, teams, and workflows on top — and I reinvest what I learn back into the open-source MCP and agent-tooling ecosystem on [GitHub](https://github.com/alex-feel/), so the internal work stays aligned with how the field is actually moving. The concrete systems this skill has shipped into — a shared retrieval backbone, an AI localization system, an agent-facing MCP server for a system-of-record platform, multi-agent research workflows, and earlier RAG and ML work — are listed in the **Related Projects**, **Related Experience**, and **Related Certifications** panels above. ## Site index - [llms.txt](https://www.alexfeel.info/llms.txt) - [Complete index](https://www.alexfeel.info/llms-index.txt)