Prompt vs. Finetune: Choosing the Right Tool for Controlling LLMs

Author(s): Wahidur Rahman

Originally published on Towards AI.

Prompt engineering a practical alternative to finetuning

Prompt engineering might sound like a complex term. But at its core, it simply means giving instructions to a generative AI tool to perform a task. Good prompt engineering often means adding context or refining your request to get more accurate and helpful results. Whether you’re asking ChatGPT to help you craft a resume or using DALL·E to create an image for a slideshow, anyone can learn to do it — no technical background required. A well-crafted prompt can turn ChatGPT into a virtual doctor diagnosing rare conditions, a lawyer drafting airtight contracts, or a poet channeling Sylvia Plath. But a poorly phrased prompt? It might yield gibberish, bias, or worse — dangerous hallucinations.

In the article, the author explores the nuances of prompt engineering as an effective approach to guiding AI tools, contrasting it with traditional finetuning methods. It discusses foundational and advanced techniques, such as Chain-of-Thought and Tree-of-Thought prompting, highlighting their impact on achieving accurate AI outputs with less effort. The economic potential of generative AI is emphasized, alongside discussions on the accessibility and efficiency of prompting techniques, making AI interaction more democratized. The article also touches on the evolving nature of AI and the significance of fostering a mindset attuned to how models interpret prompts.

Read the full blog for free on Medium.

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Published via Towards AI