GPT-based LLM workflow

A structured pipeline that chains prompt engineering, retrieval-augmented generation (RAG), and fine-tuning to automate complex reasoning tasks.

Modern GPT workflows move beyond simple chat boxes to integrate vector databases like Pinecone and orchestration frameworks like LangChain. These systems use recursive loops and multi-step reasoning (Chain of Thought) to handle high-stakes data processing. By connecting a GPT-4o model to real-time APIs and proprietary datasets, operators reduce hallucination rates below 2% and automate 80% of manual documentation tasks. The focus is on precision: using system instructions to enforce output schemas (JSON mode) and implementing automated evaluation scripts to maintain performance at scale.

https://platform.openai.com/docs/guides/optimizing-llm-accuracy