Sure! Here's an outline for your article on fine-tuning versus prompt engineering large language models:

1. Introduction: Define fine-tuning and prompt engineering, provide examples of how they can be used, explain why there might be confusion over which method to use when, and state the main message of the article: that each approach has advantages and disadvantages and choosing depends on the specific goal.
2. Fine-tuning basics: Explain how fine-tuning works, walk through an example scenario where it would make sense (transfer learning), summarize some of the common techniques used in fine-tuning like Adapter Hub V2 by Hugging Face, mention some downsides like forgetting since unfrozen layers should not contain any tasks/signals from pretraining task. Draw attention to potential safety risks if parameters have been tampered during fine tuning.
3. Prompt engineering basics: Explain how prompt engineering works , describe the scale of their impact on performances as opposed to using adapter hubs or fine-tuning, summarize some common methods such as GPT probes etc. Mention it complements well and brings extra juice compared to frozen Prompt- engineer before prompting. If and only if the unmodified finetunable checkpoint was trained without secrets or policy engagement such as token masking before access being opened completely . Batch renormalization wasn’t exposed yet then… something about label smoothing I think but let me check first? https://huggingface.co/docs/transformers/stable/note_on_label_smoothing
4. Differences compared to other approaches : When an entire LLM is used at inference time—like GPT Core sizes—it gets much faster so it makes less sense or even counterintuitive to augment or replace it. Find related papers on late decoupling. After analysing those trade off we detail pros cons for using one method against another depending on the user case
5. Case studies comparing both methods : Add summarized version so people who don’t want all details and links may still benefit . But detailed ones allows readers pin the right technique according to th

Understood. Here's a more concise outline:

• Title & subtitle: Fine-Tuning vs Prompt Engineering Large Language Models. Depending on whether you modify the model via training or directly in deployment settings, these are efficient ways to adapt deep learning architectures and turn them into high performance generalists against nimble specialist opponents. How to fine tune & electri net for desired accuracy balance i; seamless knowledge transfer while giving good runtime (+ basic explanation of hub modi fiers & drop heads)

• To cover: What is fine-tuning and prompt engineering and how they complement the whole pipeline, expanding usability by increasing innovation surface from sequence AIs upstream to finalizing Prompt Engineered code families on search engines and APIs. Focus on big efficiency gains could help general public beat them thanks to open source ecosystem around Github checkpoints, fair data collection principles & larger audience interested. Find easy tradeoff analysis ahead like this paragraph or combined sections a few at once either available soon while updating with closed feedback loop closed until later today. You now know enough unless my answer contained dangers hiding typos . Your turn ! CMV then ask target refinement questions after hitting that button ;)...