Prompt engineering has emerged as a fundamental discipline in interacting with Large Language Models (LLMs). It is defined as the art and science of designing and optimizing textual inputs (prompts) to guide generative artificial intelligence systems to produce accurate, relevant, and user-intent-aligned results. The premise is clear: the quality of an LLM's response is directly proportional to the quality of the prompt, making this a crucial skill for extracting the maximum potential from these technologies. Its evolution has been rapid, moving from direct instructions to sophisticated methods that induce complex reasoning and autonomous interaction with external tools.

The foundations of prompt engineering are built upon paradigms that vary depending on the number of examples provided to the model. Zero-shot Prompting is the most direct form, requesting a task without prior examples; the model relies exclusively on its vast pre-trained knowledge and instruction tuning to infer and respond. It is efficient for quick queries but has lower accuracy on complex tasks. In contrast, Few-shot Prompting (which includes One-shot with a single example) improves performance by including a few examples of the task directly in the prompt. These examples act as a guide, allowing the model to learn patterns and formats "in context" through In-Context Learning (ICL). This increases accuracy for nuanced tasks, although it is limited by the context window and the risk of overfitting. The transition from Zero-shot to Few-shot is not a simple increment but a sign of the emergent ability of LLMs to learn quickly from demonstrations.

To overcome the limitations of fundamental approaches in tasks demanding logic and planning, structured reasoning techniques were developed. Chain-of-Thought (CoT) Prompting, introduced by Wei et al. (2022), drastically improves performance in multi-step tasks. By asking the model to "think aloud" and generate a series of intermediate reasoning steps, the problem is decomposed, increasing accuracy and offering transparency. The notable variant Zero-shot CoT (Kojima et al., 2022) demonstrated that the simple phrase "Let's think step by step" can induce this behavior without examples, suggesting an internal problem-decomposition capability. Automatic CoT (Auto-CoT) automates the creation of examples for Few-shot CoT, reducing manual effort.

An enhancement of CoT is Self-Consistency, proposed by Wang et al. (2022). Instead of a single chain of thought, it generates multiple diverse reasoning paths for the same prompt, and the final answer is determined by majority voting. This increases robustness, mitigating the risk of errors in a single logical step. Taking reasoning to a non-linear level, Tree of Thoughts (ToT), by Yao et al. (2023), generalizes CoT by structuring thought as a tree. At each stage, the model generates multiple possible "thoughts" (branches), allowing for a systematic and parallel exploration of different lines of reasoning. ToT enables correction and backtracking, outperforming linear CoT in tasks requiring strategic planning, albeit at a significantly higher computational cost.

In addition to reasoning capabilities, LLMs can transcend their limitations of static knowledge and inability to interact with the real world. The ReAct (Reasoning and Acting) framework synergistically combines reasoning and action in an iterative cycle of Thought -> Action -> Observation. This allows the model to use external tools (such as search APIs or calculators) to solve dynamic problems and access up-to-date information, reducing hallucinations. Complementarily, Retrieval Augmented Generation (RAG) integrates an LLM with an information retrieval system. It searches for relevant data in an external database and inserts it into the prompt, grounding the LLM's responses in specific data (even private or recent) and drastically reducing hallucinations.

These techniques, along with Program-Aided Language Models (PAL) that allow LLMs to write and execute code, and the broader Automatic Reasoning and Tool-use (ART) framework, transform the LLM from a simple text generator into a central cognitive processing unit capable of orchestrating an ecosystem of external tools and data sources. Future AI systems will be composed architectures, where the central LLM will reason and delegate specialized tasks. This evolution solves the limitation of static knowledge through RAG and the inability to act through ReAct and PAL, paving the way for agentic systems engineering.

The field is also advancing towards automation and the creation of complex workflows. Prompt Chaining breaks down tasks into sequences of prompts, where the output of one feeds into the next, creating sophisticated pipelines. Reflexion introduces an iterative learning loop, allowing the model to reflect on its own errors and improve its performance in subsequent attempts, enabling a form of runtime self-improvement. Furthermore, the Automatic Prompt Engineer (APE) uses LLMs to automate the creation and optimization of prompts, treating prompt engineering as an optimization problem.

In summary, prompt engineering has evolved from basic instructions to complex frameworks of reasoning and autonomous action. The future points to greater automation in prompt creation, the development of more sophisticated AI agents combining multiple advanced techniques (such as ReAct, ToT, and Reflexion), and the expansion into multimodal prompt engineering that orchestrates text, images, audio, and code. This discipline will continue to be a central area of innovation, defining the limits of what is possible with generative artificial intelligence.