Fast answers
Use this section when you need the short answer before the deep dive. Each guide gives a practical default, the trade-offs, and a link to the longer article when the details matter.
The blog archive stays focused on long-form articles. Fast Answers are shorter decision guides.
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| Question | Short answer | Guide | Deep dive |
|---|---|---|---|
| Which agent framework should I start with? | Use LangGraph for explicit state. Use OpenAI Agents SDK for OpenAI-native Python agents. Use LlamaIndex when retrieval is central. | Best AI agent frameworks | AI agent reasoning loops |
| Which RAG evaluation stack should I use? | Start with deterministic retrieval metrics. Add Ragas, DeepEval, TruLens, LangSmith, or custom checks where they fit. | Best RAG evaluation tools and metrics | RAG evaluation metrics |
| Which local LLM tool should I use on macOS? | Start with Ollama for a local API. Use LM Studio for GUI exploration, llama.cpp for runtime control, and MLX for Apple Silicon work. | Best local LLM tools for macOS | 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. | Best OCR models for document AI | OCR guide |
| Which NER model should I use? | Use spaCy for stable labels, GLiNER for flexible zero-shot labels, Transformer token classifiers when you have training data, and LLM extraction for schema-heavy cases. | Best NER models | NER guide |
| How should I secure an AI agent? | Remove power first: narrow tools, pre-tool policy checks, sandboxing, scoped credentials, approvals, and traces. | Best AI agent security patterns | 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 on the small final set. | Best search ranking stack for AI products | Search ranking stack |