[Topic]
Custom AI workflows
Prompting, retrieval, tuning and multi-step pipelines, and how to combine them for your task.
ComparisonBeginner
Fine-tuning vs RAG vs prompt engineering: a practical comparison
Prompt engineering changes the instructions, RAG changes the context the model sees, and fine-tuning changes the model's weights. Start with prompting, add retrieval when answers depend on facts the model lacks or that change, and fine-tune when you need consistent format, style or behavior, or lower cost and latency at scale.
GuideIntermediate
How to build custom AI workflows that improve output quality
A custom AI workflow splits a task into fixed steps, each with its own prompt or model, validation and evaluation, so errors are caught where they happen instead of hidden in one long prompt. It improves quality when the task decomposes cleanly and each step can be checked; otherwise a single well-prompted call is simpler and often just as good.