I’ve been refining a hands-on approach to “burger prompting” that turns prompt engineering into a reliable, repeatable system. Using an AI resume coach as the proving ground, I’ll walk through a detailed prompt structure to get the most out of your LLM and share what’s worked for me in product environments where clarity, consistency, and measurable outcomes matter.
At a high level, burger prompting follows a simple mental model: the top bun frames the role and mission, the fillings pack in context and examples, and the bottom bun locks in output format and quality guardrails. It’s deceptively simple and extremely effective for Generative AI use cases where you need predictable behavior across different inputs and user personas.
For the top bun, I establish the AI’s role, audience, and objective in one place. In the resume coach flow, I define the assistant as a structured, unbiased reviewer tasked with aligning a candidate’s resume to a specific job description. I set constraints on tone (supportive but direct), scope (resume and job description only), and safety (avoid speculative claims, defer legal or medical advice). This crisp intent statement reduces ambiguity and prevents the model from wandering outside the product’s value proposition.
The fillings are where context window management becomes crucial. I inject the job description, the candidate’s resume, a capability rubric aligned to the role, and the company’s style preferences. If the content is long, I chunk inputs and, when needed, use a retrieval-first pipeline to fetch only the most relevant snippets. I also include a brief style guide with voice, depth, and formatting expectations so the AI doesn’t drift between terse and verbose responses across sessions.
Strong examples are the meat of the burger. I include a few annotated comparisons that show what “excellent,” “good,” and “needs improvement” look like for specific competencies, from impact statements to quantification. These examples are compact and domain-specific, so the LLM sees the pattern I expect without overfitting to a single profile. I encourage transparent reasoning by asking for stepwise evaluations that reference evidence from the resume and job description, while keeping the explanations concise and user-friendly.
The bottom bun finalizes structure and guardrails. I specify an output schema that always returns a brief summary, evidence-backed strengths, concrete gaps with examples of what’s missing, and a prioritized action plan with suggested rewrites. I also request a rubric-aligned score to support eval-driven development, and I cap length to ensure scannability inside product UI. This predictable format reduces downstream parsing errors and keeps the AI workflow snappy.
To operationalize this in a product context, I run small A/B tests on the prompt variants and measure utility through user activation and completion rates. I tune the prompt with tight feedback loops, comparing structured scores against human spot checks until the variance narrows. When I see drift, I adjust the constraints, swap underperforming examples, or expand the rubric to capture overlooked signals.
Quality and trust are non-negotiable. I add guidance to avoid hallucinated credentials or inflated claims, enforce privacy-by-design around sensitive data, and encourage the assistant to cite which resume lines support each recommendation. When the model is uncertain or the resume lacks evidence, the assistant should explicitly say so and propose realistic next steps rather than guessing.
The result is an AI resume coach that feels both helpful and disciplined. With burger prompting, you get a durable prompt pattern you can reuse across adjacent AI workflows, from portfolio reviews to job description rewrites. Once you internalize the top bun, fillings, and bottom bun, you’ll find it far easier to ship prompts that scale, maintain consistency across releases, and deliver tangible, career-advancing outcomes for users.
Inspired by this post on Pendo – Best Practices.
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