> For the complete documentation index, see [llms.txt](https://docs.senseloaf.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.senseloaf.ai/agents-output/ai-matching-report-card.md). # AI Matching Report Card AI Matching Report Card brings together all the evaluation data generated from the resume matching stage — giving you a clear, structured picture of how well a candidate fits the role based on their resume and your configured matching strategy. Use AI Matching Insights to move from initial screening to shortlisting faster, with full confidence in every decision you make. ### What's included * **Overall candidate fit** — how well the candidate matches the role overall * **Role alignment** — whether their background fits the context of the role * **Detailed evaluation scores** — how they performed against each criterion * **Resume data** — the actual information used to arrive at those scores

#### 1. Overall Summary The Matching Summary is your quick read on a candidate. It gives you an at-a-glance view of how well they match the role, based on their resume and your configured matching strategy.

**What it's based on:** * Resume evaluation * Criteria defined in your matching strategy Use this as your starting point. The summary is designed for fast decisions — it tells you within seconds whether a candidate is worth a closer look before you dive into the details. #### 2. Role Context Snapshot The Role Context Snapshot checks whether the candidate fits the *context* of the role — not just the skills on paper.

**What it covers:** * Seniority level — is their experience at the right level for this role? * Company type — have they worked in similar organizations? * Industry relevance — does their background align with your domain? This is especially useful for roles where context matters as much as skills — like senior positions or highly specialised industries. #### 3. Evaluation Breakdown The Evaluation Breakdown gives you the full picture. It shows how a candidate was scored against every individual criterion in your matching strategy, along with the reasoning behind each score.

**What it includes:** * Individual score for each criterion * A justification explaining why that score was given * How each score aligns with the matching strategy you configured Use the breakdown when the summary alone isn't enough — for borderline candidates, senior hires, or whenever you need to explain a shortlisting decision to a colleague or hiring manager. #### 4. Parsed Resume View The Parsed Resume View shows the candidate's resume in a clean, structured format — with the key information already extracted and organised.

**What it shows:** * Key information extracted from the resume * The specific data points used during evaluation Use this to verify that the evaluation signals are accurate. If a score seems unexpectedly high or low, the Parsed Resume View lets you cross-check exactly what data the system used. #### 5. Viewing the matching strategy You can also see the exact logic used to evaluate a candidate directly from within Matching Insights.

**How to access it:** 1. In the evaluation section, click **View Strategy** 2. A detailed panel opens showing: * Evaluation criteria * Measurement logic for each criterion * Scoring weights If a score seems unclear or unexpected, this helps you understand how it was calculated and validate that the strategy is being applied the way you intended. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.senseloaf.ai/agents-output/ai-matching-report-card.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.