A practical, data-driven guide comparing prompting vs. fine-tuning for LLM apps, with decision checklists, trade-offs, and implementation tips.
A practical, end-to-end guide to designing, deploying, and operating embedding-based similarity search in production.
Design a production-grade AI marketing copy generation API: architecture, prompts, guardrails, evaluation, and code examples.
A practical blueprint for building scalable, safe AI support chatbots—from NLU and RAG to orchestration, guardrails, and observability.
A practical, end-to-end guide to RAG evaluation metrics—from retrieval and grounding to faithfulness, relevance, and online impact.
Practical strategies to optimize LLM context windows—reduce cost and latency while preserving accuracy with RAG, chunking, compression, caching, and evaluation.
