--- title: "A How-To Guide On Fine-Tuning" url: https://daily.dev/posts/a-how-to-guide-on-fine-tuning-e5bdp6qez source_url: https://blog.promptlayer.com/a-how-to-guide-on-fine-tuning type: article source: "PromptLayer Blog" published: 2026-07-04T02:19:36.023Z updated: 2026-07-04T04:20:59.318Z tags: ["llm", "deep-learning", "rag", "mistral-ai"] reading_time: 9 upvotes: 2 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # A How-To Guide On Fine-Tuning **[PromptLayer Blog](https://daily.dev/sources/promptlayer)** · 9 min read · 2 upvotes · 0 comments ## Summary A practical walkthrough of LLM fine-tuning covering two examples: manually fine-tuning Mixtral 8x7B to mimic a personal writing style using notes and essays, and using PromptLayer to fine-tune GPT-3.5 as a cheaper workout generator trained on GPT-4-generated data. The guide covers data sourcing, formatting challenges, using LLMs to generate training pairs, and the iterative pain points of the manual approach versus the streamlined PromptLayer workflow. Concludes that fine-tuning is often more trouble than RAG for most use cases. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [How to Fine-Tune an LLM: An End-to-End Guide](https://daily.dev/posts/how-to-fine-tune-an-llm-an-end-to-end-guide-dgv3ruc1u) · Towards Data Science · 0 upvotes · 0 comments --- Tags: [#llm](https://daily.dev/tags/llm), [#deep-learning](https://daily.dev/tags/deep-learning), [#rag](https://daily.dev/tags/rag), [#mistral-ai](https://daily.dev/tags/mistral-ai) [View this post on daily.dev](https://daily.dev/posts/a-how-to-guide-on-fine-tuning-e5bdp6qez)