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The Transient Nature of Prompt Engineering: A Call for More Robust Language Models

In tech , sometimes hacks  live on and become permanent. It’s important to recognize when a hack is needed and set a _deadline  _with exit criteria before letting the hack live on to become a pattern some might regret.

A visually engaging concept illustration that portrays the temporary nature of 'hacks' in technology, focusing on prompt engineering as a workaround in AI language models. The scene shows a robot, resembling a large language model, standing in front of a chalkboard covered in hastily written prompts and equations. The robot holds a piece of chalk, with an expression that suggests it's contemplating a problem. Behind the robot, there’s an open door leading to a futuristic room, symbolizing the 'desired state' where AI understands human language naturally without prompt manipulation. In the foreground, the robot's chalkboard has the words 'Prompt Engineering' crossed out, and the robot is looking towards the open door with hope. The background conveys a sense of transition, from a cluttered and messy workshop towards a sleek, modern, and minimalist AI lab. The overall tone is optimistic, showing movement from hacks to innovation.

One recent  example is prompt engineering , a valuable HACK I’d say, considering the limitations of the initial versions of LLMs, e.g., data limitations, and how they were trained. Prompt engineering was (is) a stop-gap to formulate prompts/queries in a way that the LLM would understand them, similar to how they were trained and (instruction) tuned. 𝗜.𝗲., 𝗰𝗮𝗻 𝘄𝗲 𝗰𝗿𝗲𝗮𝘁𝗲 𝗽𝗿𝗼𝗺𝗽𝘁𝘀 𝘀𝗼 𝗰𝗹𝗼𝘀𝗲 𝘁𝗼 𝘁𝗵𝗼𝘀𝗲 𝗶𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹 𝘄𝗮𝘀 𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗼𝗻 𝘁𝗼 𝗿𝗲𝗮𝗹𝗶𝘇𝗲 𝘁𝗵𝗲 𝗯𝗲𝘀𝘁 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲?

The question here is, how to reduce noise to elimiate the need to engineer Human language. Imagine you’re asking a friend for help. You’d typically be direct and informal: “Hey, I could really use some help with this.” You wouldn’t be saying, “You’re a great friend, you’re supposed to help me, here’s exactly how you can help…” because that level of detail isn’t necessary. Your friend understands your context and the nature of your request without needing you to spell it all out.

I don’t see prompt engineering  going away completely (not in the short-term), but at least for foundational models , we shouldn’t have to rely on it as a pillar technique and acknowledge it as a hack, and put efforts in engineering and modelling beyond the prompt layer._ _ It’s not good UX.

TL;DR:

  • Hacks can become permanent, so be wary of prompt engineering solidifying as a standard practice.

  • Prompt engineering is a valuable but temporary fix for current LLM limitations.

  • Ideal language models understand human language regardless of prompt formatting – no engineering needed.

  • Reliance on prompt engineering highlights a UX flaw: models aren’t robust enough.

  • Instead of refining prompt engineering, focus on reducing “noise” (limitations in data, architecture, etc.).

  • Like talking to a child vs. an adult, we want models to understand implicit context, not engineered prompts.

  • Shannon’s Information Theory: prompt engineering is needed due to channel noise, which we must reduce.

  • Prompt engineering may remain relevant in niche areas, but not for foundational models.

  • Invest in engineering and modeling beyond the prompt layer for truly robust and user-friendly language AI.

  • Good UX means natural interaction – let’s move beyond prompt engineering for the future of language models.

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