A step-by-step guide to preparing high-quality datasets for LLM fine-tuning, from sourcing and cleaning to formats, safety, splits, and evaluation.
A practical guide to function calling vs. tool use in LLMs: architectures, trade-offs, design patterns, reliability, security, and evaluation.
Build a production‑ready pipeline for AI document understanding: upload, OCR, schema‑based extraction, tables, QA, validation, and storage.
Build a production-ready tutorial for knowledge graph–enhanced AI retrieval: schema, ingestion, Cypher, hybrid search, and evaluation.
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.
