Why Prompt Engineering Will Never Be As Deterministic As Programming
In the age of large language models (LLMs), prompt engineering has emerged as a new way to define behavior. But as we embrace this new paradigm, it's important to understand how it fundamentally differs from traditional programming—and why it will never offer the same level of determinism.
Defining Software Behavior: Two Worlds
At their core, both programming and prompt engineering attempt to do the same thing: define software behavior. But the tools they use and the assumptions they make diverge sharply.
Programming languages use centrally defined APIs and deterministic execution models. Every function, variable, and type must conform to a strict specification. The system behaves predictably, and small changes yield predictable results.
Prompts, on the other hand, define behavior through natural language and depend on the latent knowledge and internal mechanisms of an LLM. Execution is not deterministic, but probabilistic—dependent on model weights, training data distribution, and phrasing.
Centralized vs. Distributed Semantics
One key difference lies in how meaning is assigned:
- Traditional APIs are built with centralized semantics. They have one official definition, often documented and enforced by compilers or interpreters.
- Prompts operate with distributed semantics. Meaning is not defined by a central authority but emerges from how the LLM was trained, how the prompt is phrased, and how the model internally maps language to behavior.
What this means in practice is that the same intent may need to be expressed in multiple ways, and different LLMs may interpret those expressions differently. A function call in code behaves the same way every time; a prompt can behave differently depending on many hidden variables.
Canonical vs. Fuzzy Function Triggering
In programming, function invocation is exact:
book_flight(destination="Tokyo")
This call will only work if the function is defined with the exact name and parameter structure. There's no room for ambiguity.
In prompting, however, triggering a tool might look like this:
- "I want to go to Tokyo."
- "Can you get me a flight there?"
- "Book me a ticket to Japan."
All these phrases might trigger the same tool — or not, depending on the LLM. The triggering is heuristic, not canonical. It's based on fuzzy matching informed by training data, embeddings, and probabilistic inference.
Why Prompting Can Never Be Fully Deterministic
Even with better LLMs, prompt engineering is unlikely to reach the determinism of software engineering due to these structural differences:
- Semantic Control: Programming defines semantics centrally and precisely. Prompting relies on distributed meaning that may vary between models.
- Function Invocation: Programming requires exact input structure; prompting interprets intent loosely, often probabilistically.
- Debuggability and Predictability: Programming supports tools like static analysis and debuggers. Prompts often require trial-and-error.
This leads to differences in reproducibility, optimization strategies, and guarantees. Even if LLMs continue to improve, the inherent fuzziness in natural language and model interpretation means prompting will always involve more uncertainty.
Final Thoughts
Prompt engineering isn't a worse form of programming — it's a different paradigm. It trades strict control for flexibility and expressive power. As AI becomes more integrated into software systems, understanding these differences will be key to building reliable, effective hybrid applications.
The future may lie in blending the two: using code for precision and prompts for context, creativity, and interaction. But we should be clear-eyed about the trade-offs. Prompt engineering will never be as deterministic as programming — and that's not a flaw. It's just a different curve.