Prompt Engineering and Variation Across LLMs

In the world of software development, prompt engineering is emerging as a crucial discipline. The ability to extract accurate and useful responses from Large Language Models (LLMs) has become a competitive advantage. However, one fundamental aspect of this new craft is being largely ignored: the engineering of a good prompt can, and will, silently change depending on the model being used. Many treat LLMs as a monolithic entity, expecting a “master prompt” to work universally, but reality is much more complex and nuanced.

The truth is that no two LLMs are alike. Just as different programming languages have their own syntax and paradigms, LLMs have distinct “personalities”, shaped by their architectures, training data, and fine-tuning processes. A model trained with a massive focus on source code from public repositories will behave differently from one optimized for creative dialogue. These differences are not merely superficial; they influence the way the model “reasons”, interprets ambiguity, and structures its responses. For a developer, this means that the way you ask for a Python code snippet, the explanation of a complex algorithm, or the creation of technical documentation needs to be adapted to the specific “dialect” of the LLM in question.

This variation is the blind spot of current context engineering. We see a proliferation of guides and “recipes” for prompts, but they rarely come with a label specifying which model they were designed for. A carefully crafted prompt for generating unit tests with model A can produce mediocre or verbose results with model B. This lack of awareness can lead to cycles of frustration, where developers believe the tool is inadequate when, in reality, the communication is misaligned. Effective prompt engineering, therefore, is not about finding a magic formula, but about developing the sensitivity to understand the tendencies and particularities of the LLM you are interacting with.

For us developers, this means experimentation and adaptation are essential. When integrating an LLM into a workflow, whether for code automation, log analysis, or customer support, you need to invest time in “learning” the model. Testing variations of the same prompt, observing how it handles missing context or complex instructions, and documenting what works best is a step that cannot be skipped. Prompt engineering in software development must evolve into a practice more like performance optimization: a continuous process of measurement, adjustment, and refinement, always considering the unique characteristics of the platform being used. The next frontier is not just creating good prompts, but creating the right prompts for the right brain.

Non-Determinism and the “Personalities” of Models

Not all LLMs are created equal. But that statement only scratches the surface. For a software developer, understanding why this variation exists is what turns frustration into productivity. The question goes far beyond training data; it lies in architecture, fine-tuning philosophy, and, crucially, in an inherent characteristic of these models: controlled non-determinism.

Imagine the following scenario: you ask two different LLMs to “create a Python function that validates a CPF”. Model A returns lean, idiomatic code, using a regular expression and no comments, assuming you are an experienced developer. Model B, for the exact same prompt, delivers a much longer function, with step-by-step validation, try-except blocks, type hints, and detailed comments explaining the logic behind each check digit. Neither is “wrong”, but they operate under different philosophies. Model A may have been optimized for concision and use in competitive programming environments, while Model B was tuned with a bias toward safety, clarity, and educational purposes, prioritizing code that is easy to understand and maintain, even if more verbose. These “personalities” are the direct result of how the models were refined after initial training, a process that teaches not only what to answer, but how to answer.

Now let us add an even more intriguing layer: non-determinism. You run the same prompt, on the same Model A, twice in a row and get two slightly different variations of the code. This is not a bug; it is one of the most powerful and misunderstood characteristics of LLMs. Most models expose a parameter called “temperature”. Think of temperature as a “creativity knob” or a “risk manager”. With temperature at zero, the model becomes “deterministic” (or as close to it as possible): it will always choose the next word or token with the highest statistical probability. The result is more predictable and repetitive, useful for tasks that require absolute consistency.

However, when we increase temperature, we introduce calculated randomness. The model becomes able to choose words that are not the most probable but still make sense in context. It is like a blues musician improvising over a melody. The basic structure is there, but the notes chosen at each moment can vary, creating a unique piece on every run. For a developer, this is gold. When asking an LLM to “suggest ways to refactor this code snippet”, a temperature greater than zero can generate three or four valid and creative approaches instead of only the most obvious and statistically common one. It may suggest using a design pattern you had not considered or a more modern library function.

This dance between a model’s fixed “personality” and the fluidity of its non-deterministic output is the core of advanced prompt engineering. It means our work is not just sending an instruction and receiving a response. It is understanding that we are starting a dialogue with a tool that has its own biases and an element of controlled creativity. The perfect prompt, therefore, is not static. It may need adjustments depending on whether you are talking to the “verbose architect” or the “minimalist coder”. And sometimes the best solution comes from asking the same question a few times, allowing the “luck” of non-determinism to show you an unexpected and innovative path.