AI
5 sub-sections
AI#
Practical reference for building with and using large language models — Anthropic’s Claude Code CLI, OpenAI’s Codex CLI, the Claude API, and foundational prompt engineering and RAG techniques.
What’s in this section#
| Sub-section | Pages |
|---|---|
| Claude Code | Overview · Slash Commands · Settings · Hooks · MCP Servers · Power-User Tips |
| Codex | Overview · Configuration · Slash Commands · Approvals & Sandbox · MCP Servers · Tips & AGENTS.md |
| Claude API | Python SDK · Tool Use (Function Calling) |
| Prompting | Prompt Engineering Patterns · RAG Implementation Checklist |
| Frameworks | Transformers · Gemini SDK · NotebookLM · LangChain · LlamaIndex · AutoGen · CrewAI · Guidance · ChromaDB · LangSmith · ragas · TruLens · unstructured · Weaviate · Qdrant |
Sections#
- Claude Code — Anthropic’s agentic CLI: install, configure, extend, and automate.
- Codex — OpenAI’s open-source coding agent: TOML config, sandboxing, slash commands, and AGENTS.md.
- Claude API — Python SDK reference: streaming, vision, caching, batch, and tool use.
- Prompting — Reliable prompt patterns and an end-to-end RAG implementation checklist.
- Frameworks — Hugging Face Transformers, Gemini SDK, NotebookLM, LLM application frameworks (LangChain, LlamaIndex, AutoGen, CrewAI, Guidance), observability and evaluation (LangSmith, ragas, TruLens), document ingestion (unstructured), and vector databases (ChromaDB, Weaviate, Qdrant).
Claude Code
10 cheat sheets
Codex CLI
8 cheat sheets
Claude API
7 cheat sheets
Prompting
6 cheat sheets
Frameworks
9 cheat sheets
Articles in this section (12)
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.
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.
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.
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.
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.
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.
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.
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.
unstructured — Document Parsing & Ingestion
Extract structured text from PDFs, Word docs, HTML, images, and more with the unstructured library. Covers partitioning, chunking, cleaning, metadata, and pipeline integrations.
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.
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.
Sentence Transformers — Embeddings, Search & Fine-Tuning
Comprehensive reference for the sentence-transformers Python library — embeddings, similarity, clustering, retrieval, fine-tuning, and popular models (BGE, E5, GTE, Nomic, Jina).