I am experimenting with advanced prompt engineering techniques to classify customer reviews. When applying the <prompt engineering> methodologies, I noticed my few-shot examples often bias the output if the formatting is inconsistent. How do you properly isolate context from data variables? Should I use Markdown wrappers or structural XML blocks to prevent prompt injection?
3 answers
Structuring few-shot examples demands rigorous syntax isolation to avoid context contamination. The most reliable convention is wrapping structural segments inside explicit XML blocks like <example> and <response>. This distinct delineation helps the model decode boundaries cleanly, ensuring it interprets variables strictly as raw input rather than subsequent user instructions. Additionally, shuffle your training examples dynamically between production calls to prevent the underlying transformer architecture from favoring the final label provided in your sequential sequence.
What precise delimiter format are you executing between different dynamic variables inside your prompt workspace to verify that your data inputs are never processed as operational instructions?
You should balance your dynamic example arrays perfectly across every single classification category so the model doesn't develop an implicit bias toward a dominant response label.
Alan is exactly right. If your few-shot sequence contains three positive reviews and only one negative example, the underlying transformer weights lean heavily toward predicting positive outcomes during inference.
I typically utilize triple backticks or distinct JSON key-value blocks within my template configuration. This explicitly signals to the LLM processor that the contained content is an isolated text payload. This structural pattern effectively halts any downstream token interpretation errors and prevents malicious user injections from overriding system level rules.