A practical guide to integrating an AI writing assistant via API—architecture, prompt design, code samples, safety, evaluation, and performance optimization.
Practical, end-to-end guide to deploying open-source LLMs—from model choice and hardware sizing to serving, RAG, safety, and production ops.
Hands-on guide to reliable, secure tool calling for AI agents: architecture, schemas, control loops, error handling, observability, and evaluation.
A practical guide to advanced chunking in RAG: semantic and structure-aware methods, parent–child indexing, query-driven expansion, and evaluation tips.
Build a production-ready LangGraph multi-agent workflow with a supervisor, tools, checkpointing, and streaming—step-by-step with tested Python code.
Step-by-step DeepSeek API integration: base URL, models, cURL/Python/Node code, streaming, thinking mode, tool calls, errors, and production tips.
