newdata.cloud

A practical guide to LLM context window limits, common failure modes, and reliable ways to design around them.

A reusable prompt injection prevention checklist for securing RAG systems, agents, and tool-using LLM apps before launch and after workflow changes.

A practical framework for choosing embedding models for search, clustering, and RAG using task fit, cost, multilingual support, and migration tradeoffs.

A reusable framework for tracking open-source AI developer tools, from eval libraries to prompt utilities and web tooling.

A practical comparison of LangSmith, Promptfoo, TruLens, DeepEval, and related prompt testing frameworks for LLM app teams.

A practical guide to choosing response, semantic, or retrieval caching for LLM apps based on cost, latency, freshness, and quality risk.

A practical framework for comparing vector databases for RAG by retrieval performance, filtering needs, and total cost fit.

A practical comparison guide to prompt testing frameworks for LLM apps, including key features, tradeoffs, and scenario-based buying advice.

A practical guide to JSON mode, schemas, tool calling, and validation patterns for reliable structured LLM output.

A practical RAG debugging checklist to reduce hallucinations by improving retrieval, context assembly, prompting, and evaluation.

A practical framework for comparing OpenAI, Anthropic, and Gemini API costs beyond token prices, including rate limits, retries, and real workload fit.

A practical buyer’s guide to LLM evaluation tools, with a repeatable way to compare features, fit, and total cost.

A practical guide to monitoring LLM apps in production with metrics, traces, eval loops, and failure-mode tracking.

A practical guide to choosing between function calling, tool calling, and JSON output for reliable LLM integration.

A practical guide to building and maintaining an LLM evaluation dataset that stays relevant as prompts, products, and models change.

A practical guide to prompt engineering techniques that still improve reliability across model changes, from constraints to structured self-checks.

A practical tutorial for building a prompt regression test suite that catches production LLM failures before they reach users.