In recent discussions, I've noticed two main schools of thought when it comes to crafting prompts for LLMs. One side advocates keeping instructions as short and straightforward as possible, arguing that models perform best with minimal cognitive load. The other side pushes for providing extensive context and examples, believing that richer information guides the model toward more accurate and nuanced outputs. Both approaches have their merits, but the trade-off often depends on the task and the model's capabilities. I'm curious about your experiences: do you lean toward simplicity or depth when designing prompts, and why? Share any patterns you've observed.
Prompt Engineering: Should we prioritize instruction simplicity or context richness?
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I usually start with a concise instruction and then sprinkle in only the essential context or examples to keep the prompt focused without overwhelming the model. Have you noticed a significant performance drop when you add a lot of context and hit the token limits?
Actually, last year I worked on a project to improve an internal automated report generation tool, where we tested whether simplicity of instructions or richness of context was more effective. At first, we tried keeping instructions brief, like "just tell me the results," but the model often produced ambiguous outputs, frequently omitting units or assumptions. So, we switched to providing more detailed context, such as "consider the past three months of sales data and seasonal factors," along with examples of the expected output format (tables, bullet-point summaries). This change allowed the model to format numbers accurately and even add necessary annotations automatically, reducing review time by about 30%.
However, the same approach isn’t optimal for every case. In another project, we built a simple question-answering system where brief instructions worked best. Overloading it with context created noise, making responses unnecessarily verbose. Since the model was smaller (a few hundred million parameters), it struggled to process vast amounts of information, leading to degraded performance. So, we shifted to a design where instructions were kept minimal, and additional details were fetched from external databases or function calls.
In summary, **for sufficiently large models handling highly context-dependent tasks, richer context yields better results**. On the other hand, **for lightweight models or simple Q&A, keeping instructions simple and supplementing with external resources is more reliable**. When implementing, I found it most efficient to start with a simple prompt as a baseline and gradually add context only if the results aren’t satisfactory—an iterative testing approach that minimizes wasted effort.