Fast Answers
This section is for readers who need the quick version first. Each guide gives a default choice, the trade-offs, and a pointer to the deeper article when the details matter.
The regular blog archive stays focused on long-form articles. These pages are the shorter companion layer, not a replacement for the deep dives.
Start here
| Question | Fast answer | Deeper article |
|---|---|---|
| Which agent framework should I start with? | Use LangGraph for explicit state, OpenAI Agents SDK for OpenAI-native Python agents, LlamaIndex for RAG-heavy agents, CrewAI for role-based workflows. | AI agent reasoning loops |
| Which RAG evaluation stack should I use? | Use deterministic retrieval metrics first, then add Ragas, DeepEval, TruLens, LangSmith, or custom checks where they fit. | RAG evaluation metrics |
| Which local LLM tool should I use on macOS? | Start with Ollama, use LM Studio for GUI exploration, llama.cpp for runtime control, and MLX for Apple Silicon-native work. | Local LLMs on macOS |
| Which OCR model should I use for document AI? | Use cloud VLMs for complex documents, classical OCR for clean printed text at scale, and open VLMs when data control matters. | OCR guide |
| Which NER model should I use? | Use spaCy for stable known labels, GLiNER for flexible zero-shot labels, Transformers for trained token classification, and LLM extraction for schema-heavy cases. | NER guide |
| How should I secure an AI agent? | Remove power first: narrow tools, pre-tool policy checks, sandboxing, scoped credentials, approvals, and traces. | AI agent security |
| What search ranking stack should I build? | Start lexical, add dense recall, fuse results, rerank with a cross-encoder, and use LLM reranking only at the narrow end. | Search ranking stack |