The Four Paradigms of Prompting
Introduction
Large language models (LLMs) map sequences of text to other sequences of text. Given an input string, an LLM predicts the most probable continuation based on the statistical patterns it has learned during its training process. What makes LLMs remarkable is not merely their predictive capability, but the emergent behaviors they display across a wide range of tasks including
- Correct spelling and punctuation
- Syntactic and grammatical structure
- Semantic understanding and paraphrasing
- Conversational coherence and dialogue flow
- Multilingual expression and translation
- Code generation and formal logic
- Question answering and knowledge retrieval
We can use these capabilities to solve complex problems, but to do so effectively, we need to understand how to communicate with these models.
Podcast
If you prefer listening over reading, check out this podcast episode where the four prompting paradigms are explored in more detail.
The Fragility of Model Behavior
At the heart of the prompt engineering challenge is the sensitivity of LLMs to input phrasing. Unlike traditional deterministic systems, LLMs do not "understand" tasks in the human sense; they generate the most statistically probable continuation of a given sequence. This makes their output highly sensitive to subtle changes in prompt design.
For instance, in instruction-based prompting, the difference between "Summarize this article" and "Write a short abstract" can cause the model to shift tone, structure, or depth of analysis. This brittleness necessitates a thoughtful and experimental approach to prompt construction.
Prompt engineering is therefore essential not only for eliciting correct outputs, but also for:
- Preventing hallucinations
- Enforcing output structure
- Ensuring task fidelity
- Aligning with domain-specific requirements
Each of the four paradigms offers unique affordances and constraints for prompt construction.