Engineering Blog
Deep dives into RAG pipelines, prompt engineering, fine-tuning, and the architecture decisions that matter for production AI.
A comprehensive, hands-on guide to building production-ready Retrieval-Augmented Generation pipelines. Covers embeddings, vector stores, chunking strategies, reranking, and evaluation.
Beyond basic prompting: battle-tested patterns for reliable LLM outputs in production systems. Structured output, chain-of-thought, self-consistency, guardrails, and more.
A practical decision framework for choosing between fine-tuning and RAG. Covers cost analysis, performance tradeoffs, hybrid approaches, and real-world case studies.
Our blog scratches the surface. The full TrainIQ program gives you 8 comprehensive modules, hands-on labs, and the skills to build production AI systems.