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#ai (24)

Concepts (2)

AI Agents

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.
llm ai tools mcp tool-use autonomy react-pattern multi-agent web-researched

Retrieval-Augmented Generation (RAG)

Grounding LLM responses in chunks retrieved from an external corpus so the model reasons over real, citable sources instead of parametric memory alone.
llm vector-search ai grounding embeddings hallucination evaluation web-researched

Cheat sheets (22)

Agent Frameworks Comparison — Decision Matrix

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.
ai llm agents langchain llamaindex autogen crewai haystack semantic-kernel comparison decision-matrix

DSPy — Programmatic Prompting and Optimisation

Build LLM programs in DSPy with declarative signatures, modules, and optimisers. Covers Predict, ChainOfThought, ReAct, BootstrapFewShot, COPRO, MIPRO, MIPROv2, and inference compilation.
python dspy llm ai prompting optimisation stanford signatures modules

Haystack 2.x — Pipelines for LLM Applications

Build production-grade LLM pipelines with Haystack 2.x. Covers components, the pipeline graph, indexing and querying, retrievers, generators, RAG patterns, and evaluation.
python haystack llm rag ai pipelines deepset retrievers generators

MCP Frameworks — Servers, Clients, and Transports

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.
mcp model-context-protocol anthropic python typescript go fastmcp tools sse stdio ai

Semantic Kernel — Microsoft's AI Orchestration SDK

Build LLM-powered applications with Microsoft Semantic Kernel. Covers the kernel, plugins, prompt templates, planners, function calling, Kernel Memory, Python and .NET SDKs.
python dotnet csharp semantic-kernel microsoft llm ai plugins planners memory

Prompt Engineering Patterns

Reliable prompt structures for reasoning, extraction, classification, generation, extended thinking, and vision tasks with Claude.
prompting llm ai claude chain-of-thought xml thinking

RAG Implementation Checklist

End-to-end checklist and code for building reliable Retrieval-Augmented Generation pipelines — chunking, embedding, vector DBs, retrieval, and evaluation.
rag ai embeddings vector-db llm retrieval semantic-search

Frameworks

Hugging Face Transformers, LangChain, Google Gemini SDK, and LangSmith — practical reference for AI/ML frameworks and observability tools.
ai llm python ml transformers langchain gemini

AI

Claude Code, Codex CLI, the Claude API, and prompt engineering — practical reference for building with and using large language models.
ai llm claude prompting anthropic codex openai

AutoGen — Multi-Agent Conversations

Build multi-agent AI systems with Microsoft AutoGen. Covers agents, group chats, code execution, tool registration, async runtimes, and LLM configuration.
python autogen agents llm ai microsoft multi-agent code-execution

ChromaDB — Embedded Vector Database

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.
python chromadb vector-database embeddings rag ai similarity-search

crewAI — Role-Based Agent Crews

Orchestrate teams of role-playing AI agents with crewAI. Covers agents, tasks, crews, tools, LLM selection, memory, YAML config, and the kickoff lifecycle.
python crewai agents llm ai multi-agent rag tools

google-generativeai — Gemini SDK

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.
python google gemini llm ai multimodal api

guidance — Constrained LLM Generation

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.
python guidance llm ai structured-output constrained-generation transformers

LangChain — LLM Application Framework

Build LLM-powered pipelines with LangChain. Covers LCEL chains, chat models, prompts, output parsers, tools, agents, retrievers, memory, and streaming.
python langchain llm ai rag agents openai anthropic

LangSmith — LLM Observability & Evaluation

Trace, debug, evaluate, and monitor LLM applications with LangSmith. Covers tracing setup, datasets, evaluators, prompt hub, comparing runs, and CI integration.
python langsmith llm observability tracing evaluation langchain ai

LlamaIndex — Data Framework for LLMs

Build RAG pipelines and LLM-powered data applications with LlamaIndex. Covers document loading, indexing, query engines, custom LLMs and embeddings, persistent storage, and agents.
python llamaindex llm rag ai embeddings agents retrieval

notebooklm-py — Unofficial NotebookLM Client

Automate Google NotebookLM from Python with the unofficial notebooklm-py library. Covers authentication, notebook and source management, summaries, FAQ generation, and audio podcast creation.
python notebooklm google ai automation unofficial

qdrant-client — High-Performance Vector Database

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.
python qdrant vector-database embeddings rag search ai

ragas — RAG Evaluation Framework

Measure and improve RAG pipeline quality with ragas. Covers faithfulness, answer relevancy, context precision, context recall, dataset format, LLM judges, and CI integration.
python ragas rag evaluation llm ai retrieval metrics

TruLens — LLM App Evaluation

Evaluate and monitor LLM applications with TruLens. Covers the RAG triad, feedback functions, TruChain, TruLlama, custom evaluators, the dashboard, and CI integration.
python trulens rag evaluation llm ai observability feedback

weaviate-client — Vector Database Client

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.
python weaviate vector-database embeddings rag search ai