A practical guide to selecting, optimizing, and operating small language models for edge deployment—latency, memory, tooling, and MLOps.
A practical, end-to-end guide to detecting and mitigating hallucinations in LLM outputs, from uncertainty signals to retrieval-based verification.
A practical guide to designing, implementing, and governing AI chatbot personality customization—traits, prompts, memory, guardrails, and evaluation.
Build an AI-powered competitor analysis API with RAG, embeddings, orchestration, and guardrails—architecture, code patterns, KPIs, and governance.
Build a practical GraphRAG pipeline: extract a knowledge graph, index nodes and chunks, retrieve local paths and global summaries, and synthesize grounded answers.
A practical, data-driven guide comparing prompting vs. fine-tuning for LLM apps, with decision checklists, trade-offs, and implementation tips.
