LearnAIbyAmir · Topics

Multi-agent patterns, tools, and workflows for building collaborative AI systems with AutoGen.

Patterns for coordinating “crews” of specialized agents to solve complex tasks.

Design and orchestration notes for building copilots using Microsoft Copilot Studio.

Google Cloud’s low‑code platform for building chatbots and voicebots, with design best practices.

Hands-on guide to building low-code chatbots and voice bots on AWS using Amazon Lex.

Comparison and decision guide to choose between low-code AI frameworks like Lex, Dialog CX, Copilot Studio, and more.

Side‑by‑side exploration of graph- and chain-based LLM application patterns.

The same RAG + agent chatbot built twice, compared file by file: hand-written Python on the left, LangChain + LangGraph on the right, with what changed and why.

A component-by-component reference for production agentic systems: problem, technique, trade-offs, implementation, checklist, anti-patterns, and an interview soundbite for each layer.

Beginner-ready map that shows which tools to use for each part of an LLM / agent project (data, prompts, testing, deployment, monitoring), and whether they fit local testing, production, or both.

Practical walkthrough of Model Context Protocol (MCP) and how to wire external tools into LLM agents in a structured, safe way.

Full developer learning guide covering every layer of a production RAG chatbot — config, DTOs, LLM wrappers, embeddings, vector DB, Redis memory, retrieval, reranking, FastAPI, and React UI.

Full learning guide for Model Context Protocol — architecture, boilerplate code, tool creation, Claude Desktop integration, agent frameworks, deployment, and troubleshooting.

Discovery to production — 9-step flow, ADRs, observability, edge case testing, tool selection matrix, and handoff packages.

Critical SQL and database knowledge for production AI systems — indexing, query performance, concurrency, and ML-specific patterns.

How SQLAlchemy models, sessions, CRUD, and relationships work — and exactly where it sits next to Pydantic in a FastAPI app.

Stage-by-stage evolution from a single server to a globally distributed system — every scaling problem paired with its solution.

Scenario-based notes and examples to prepare for the Azure AI Engineer Associate exam.

Interactive mind-map of every Azure AI service — explore, expand, and zoom through the full ecosystem.

Full learning guide: security, warehouses, loading, performance, advanced features, time travel, privileges reference, cheat sheet, and SQL best practices.

Copy‑ready connection strings across SQL, NoSQL, vector DBs, cloud services, and messaging systems.

Consultation-level guide for choosing AI architectures and patterns for real-world solutions.

Interactive guide to choose hosting, infrastructure, and deployment tooling for AI and web projects.

Mental models, core components, Dockerfile patterns, Docker Compose, and AI/ML-specific usage including GPU and RAG pipelines.

Essential Git pipeline — branching, committing, pushing, merging, undoing mistakes, and a quick-reference command table.

Deep dive into Python's method decorators — abstract contracts, static utilities, import sources, and real-world BaseAgent patterns.

Complete testing framework for AI projects — data validation, unit tests, integration tests, RAGAS model evaluation, performance benchmarking, and debugging patterns.