I turned the playful idea of “burger prompting” into a rigorous framework for building an AI resume coach that delivers consistent, high‑quality guidance. In product management, repeatability matters: I want dependable LLM behavior, tight control of outputs, and measurable outcomes. This approach gives me exactly that—clear roles, crisp constraints, and an evaluation loop that raises the quality bar with each iteration.
Here’s the metaphor in practice. The top bun sets the role and goal; the middle layers stack context, examples, constraints, and tools; the patty is the core algorithm and output schema; and the bottom bun locks in the quality bar and follow-up behavior. When I apply this structure to an AI resume coach, I get results that feel expert, empathetic, and actionable—without rewriting the prompt every time.
Top bun: I define the system role and success criteria. I’ll say, “Act as an experienced hiring manager and resume coach for SaaS product roles” and specify the north star: improve clarity, impact, and ATS alignment without fabricating experience. I also name the audience (mid-career PMs, early-career candidates, or executives) so tone and calibration stay consistent across sessions.
First layer: I load precise context. That includes the candidate’s resume, the target job description, and any constraints (for example: keep bullets under 22 words, lead with impact, quantify outcomes). I also clarify non-goals (no inflated titles, no unverifiable claims). This is where I set the voice: confident, concise, and supportive, not generic or robotic.
Second layer: I attach the tools and references that anchor outputs. A skill taxonomy for product roles, a style guide for resume bullets, and a scoring rubric (impact, clarity, relevance, keyword coverage) help the model prioritize. To protect quality, I call out context window management rules—what to include or trim—and how to summarize long inputs without losing signal.
Third layer: I add exemplars. Few-shot examples of excellent resume bullets (“before” and “after”) teach the model what “great” looks like. I also include a counterexample or two to prevent bad habits (for instance, over-indexing on buzzwords). Exemplars act like taste buds; they steer nuance without overfitting.
Patty: I define the core algorithm and the output schema. The algorithm moves in stages: diagnose the resume against the job, identify 3–5 high-leverage improvements, rewrite bullets with quantified outcomes, and propose a summary that highlights relevant wins. I then specify the output sections: a brief diagnosis, rewritten bullets mapped to the job’s requirements, an ATS keyword coverage table, and a confidence score with rationale. A tight schema produces consistent, scannable outputs that are easy to evaluate—and easy to ship.
Bottom bun: I lock in the quality bar and the follow-up behavior. If inputs are incomplete, the coach must ask clarifying questions before rewriting. If claims lack evidence, it should suggest proof points (metrics, scope, stakeholders) rather than embellish. Finally, I require a self-check pass where the coach verifies that each bullet demonstrates impact, relevance, and clarity before presenting the final result.
Implementation blueprint: I create a reusable prompt template with clear system and user sections, then parameterize it for different roles (PM, design, data). If I have a library of style guides or skill matrices, I wire it into a retrieval layer so the model references the right material for each job. This setup makes the coach portable across tools and easy to maintain as the taxonomy evolves.
Evaluation and iteration: I practice eval-driven development. I assemble a small, representative test set of resumes and job descriptions, define acceptance criteria (readability score, keyword coverage, human rater alignment), and A/B test prompt variants. I track drift and tighten the schema whenever outputs start to meander. The goal isn’t just impressive demos—it’s reliable performance at scale.
Governance guardrails: A trustworthy resume coach respects privacy-by-design. I strip PII where possible, avoid storing raw resumes beyond what’s necessary, and document bias checks so advice doesn’t disadvantage non-traditional candidates. Clear data governance and risk management keep the product shippable and compliant as it grows.
When I apply burger prompting end to end, the AI resume coach becomes a repeatable system: fast, accurate, and measurably helpful. The structure teaches the model how to behave; the evals keep it honest; and the schema makes the result easy to review, refine, and ship. If you want dependable LLM outcomes, start with a great bun—and don’t skimp on the patty.
Inspired by this post on Pendo – Best Practices.
