Fast Answers for AI Engineering Decisions
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.
AI agents
| Question | Short answer | Guide | Deep dive |
|---|
| Which agent framework should I start with? | Use LangChain agents for a high-level start, LangGraph for explicit state, or a framework aligned with the product’s retrieval and platform needs. | Best AI Agent Frameworks | AI Agent Reasoning Loops |
| Which agent evaluation tool should I use? | Use Phoenix for open tracing, LangSmith for a managed workflow, DeepEval for Python CI, or Promptfoo for local matrix and red-team tests. | Best AI Agent Evaluation Tools | Agent Evals: Traces to Test Suites |
| How should I secure an AI agent? | Remove power first: narrow tools, pre-tool policy checks, sandboxes, scoped credentials, approvals, egress controls, and traces. | AI Agent Security Checklist | AI Agent Security |
| Which tool interface should I use? | Use function calling inside one app, MCP for reusable servers, a CLI for mature commands, or skills for reusable workflow instructions. | AI Agent Tool Interfaces | AI Agent Tool Use |
Model engineering and infrastructure
| Question | Short answer | Guide | Deep dive |
|---|
| Which local LLM tool should I use on macOS? | Compare Ollama, LM Studio, llama.cpp, and MLX-LM by workflow, artifact, runtime control, and memory headroom. | Local LLM Tools on macOS | Local LLMs on macOS |
| Which quantization representation should I use? | Choose the runtime and hardware first; GGUF, AWQ, GPTQ, FP8, and NF4 describe different layers or goals. | LLM Quantization Formats | Model Quantization Guide |
| Should I prompt, use RAG, or fine-tune? | Prompt for instructions, use RAG for changing knowledge and evidence, and fine-tune for repeatable behavior. | Fine-Tuning vs RAG vs Prompting | LLM Fine-Tuning Guide |
Retrieval and language systems
| Question | Short answer | Guide | Deep dive |
|---|
| Which RAG evaluation stack should I use? | Start with deterministic stage metrics, then add calibrated judges and product-specific checks. | Best RAG Evaluation Tools | RAG Evaluation Metrics |
| Which OCR model should I use for document AI? | Use classical OCR for clean print, a specialized parser for structured pages, and a document VLM when visual reasoning is required. | Best OCR Models | OCR Guide |
| Which NER model should I use? | Use spaCy for stable labels, GLiNER for changing labels, a supervised encoder when you have data, or an LLM for reasoning-heavy records. | Best NER Models | NER Guide |
| What search ranking stack should I build? | Start with BM25, add dense recall, fuse complementary results, then rerank a latency-bounded shortlist. | BM25 vs Embeddings vs Rerankers | Search Ranking Stack |