LLM-driven systems that pursue a goal by interleaving reasoning, tool calls, and observations inside a loop — and that decide for themselves which step to take next.
Grounding LLM responses in chunks retrieved from an external corpus so the model reasons over real, citable sources instead of parametric memory alone.
Side-by-side comparison of LangChain, LlamaIndex, AutoGen, CrewAI, Haystack, and Semantic Kernel for building LLM-powered applications and agent systems. Covers strengths, weaknesses, and when to pick each.
Build LLM programs in DSPy with declarative signatures, modules, and optimisers. Covers Predict, ChainOfThought, ReAct, BootstrapFewShot, COPRO, MIPRO, MIPROv2, and inference compilation.
Build production-grade LLM pipelines with Haystack 2.x. Covers components, the pipeline graph, indexing and querying, retrievers, generators, RAG patterns, and evaluation.
Model Context Protocol (MCP) framework overview. Covers client/server architecture, stdio vs SSE vs streamable HTTP transports, FastMCP, mcp-go, the Python and TypeScript SDKs, and comparison with custom tool servers.
Build LLM-powered applications with Microsoft Semantic Kernel. Covers the kernel, plugins, prompt templates, planners, function calling, Kernel Memory, Python and .NET SDKs.
Reliable prompt structures for reasoning, extraction, classification, generation, extended thinking, and vision tasks with Claude.
End-to-end checklist and code for building reliable Retrieval-Augmented Generation pipelines — chunking, embedding, vector DBs, retrieval, and evaluation.
Hugging Face Transformers, LangChain, Google Gemini SDK, and LangSmith — practical reference for AI/ML frameworks and observability tools.
Claude Code, Codex CLI, the Claude API, and prompt engineering — practical reference for building with and using large language models.
Build multi-agent AI systems with Microsoft AutoGen. Covers agents, group chats, code execution, tool registration, async runtimes, and LLM configuration.
Store and query vector embeddings locally or over a network with ChromaDB. Covers client types, collections, add, query, metadata filters, embedding functions, and LangChain/LlamaIndex integration.
Orchestrate teams of role-playing AI agents with crewAI. Covers agents, tasks, crews, tools, LLM selection, memory, YAML config, and the kickoff lifecycle.
Call Google's Gemini models from Python for text, multimodal, streaming, chat, function calling, and embeddings. Covers the genai SDK, safety settings, file API, and async usage.
Interleave Python control flow with LLM generation and enforce structured output using guidance. Covers gen(), select(), chat blocks, regex constraints, JSON schemas, and token healing.
Build LLM-powered pipelines with LangChain. Covers LCEL chains, chat models, prompts, output parsers, tools, agents, retrievers, memory, and streaming.
Trace, debug, evaluate, and monitor LLM applications with LangSmith. Covers tracing setup, datasets, evaluators, prompt hub, comparing runs, and CI integration.
Build RAG pipelines and LLM-powered data applications with LlamaIndex. Covers document loading, indexing, query engines, custom LLMs and embeddings, persistent storage, and agents.
Automate Google NotebookLM from Python with the unofficial notebooklm-py library. Covers authentication, notebook and source management, summaries, FAQ generation, and audio podcast creation.
Store and search vector embeddings with the Qdrant Python client. Covers collections, CRUD, filtered vector search, payload indexing, batch upsert, sparse/dense hybrid search, and integrations.
Measure and improve RAG pipeline quality with ragas. Covers faithfulness, answer relevancy, context precision, context recall, dataset format, LLM judges, and CI integration.
Evaluate and monitor LLM applications with TruLens. Covers the RAG triad, feedback functions, TruChain, TruLlama, custom evaluators, the dashboard, and CI integration.
Store, search, and manage vector embeddings with the Weaviate Python client. Covers collections, CRUD, vector/hybrid/BM25 search, multi-tenancy, generative search, and batch import.
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