Prompt engineering refers to the process of designing and refining prompts for generative AI models to produce high-quality, context-aware responses. This involves crafting clear, specific and relevant prompts tailored to the task or application to optimize the model’s output. As LLM capabilities evolve, particularly with the emergence of reasoning models, prompt engineering practices must also adapt. Based on our experience with AI code generation, we’ve observed that few-shot prompting may underperform compared to simple zero-shot prompting when working with reasoning models. Additionally, the widely used chain-of-thought (CoT) prompting can degrade reasoning model performance — likely because reinforcement learning has already fine-tuned their built-in CoT mechanism.
Our hands-on experience aligns with academic research, which suggests "advanced models may eliminate the need for prompt engineering in software engineering." However, traditional prompt engineering techniques still play a crucial role in reducing hallucinations and improving output quality, especially given the differences in response time and token costs between reasoning models and general LLMs. When building agentic applications, we recommend choosing models strategically, based on your needs, while continuing to refine your prompt templates and corresponding techniques. Striking the right balance among performance, response time and token cost remains key to maximizing LLM effectiveness.
Prompt engineering refers to the process of designing and refining prompts for generative AI models to obtain high-quality responses from the model. This involves carefully crafting prompts that are specific, clear and relevant to the desired task or application in order to elicit useful outputs from the model. Prompt engineering aims to enhance large language model (LLM) capabilities in tasks like question answering and arithmetic reasoning or in domain-specific contexts. For software creation, you might use prompt engineering to get an LLM to write a story, an API or a test suite based on a brief conversation with a stakeholder or some notes. Developing effective prompting techniques is becoming a valuable skill in working with AI systems. There is debate over whether prompt engineering is an art or science, and potential security risks, such as “prompt injection attacks,” should be considered.
