{"appId":"prompt-engineering-app","version":3,"selectedAngularVersion":20,"item":{"id":"prompt-lesson-165","conceptKey":"prompt-topic-19-learning-assistant","subjectId":"prompt-topic-19-learning-assistant","title":"Learning Assistant","summary":"Learn Learning Assistant as part of Real-World Prompt Patterns, with clear concepts, practical prompt examples, common mistakes, verification steps, and production considerations.","baseContent":"<h2>Learning Assistant</h2><p>Learn Learning Assistant as part of Real-World Prompt Patterns, with clear concepts, practical prompt examples, common mistakes, verification steps, and production considerations.</p><h3>Why it matters</h3><p>Learning Assistant turns an unclear request into an AI task whose output can be reviewed and measured. The goal is reliable communication with explicit boundaries.</p><h3>Core idea</h3><p>State the outcome, supply relevant context, add constraints, and define the expected result. Treat every response as a draft that needs verification.</p><h3>Beginner workflow</h3><ol><li>Write one clear goal.</li><li>Add only necessary context.</li><li>State limits and exclusions.</li><li>Request a specific format.</li><li>Review facts and test the result.</li></ol><h3>Easy example</h3><pre><code>Explain this concept to a beginner in five bullet points. Include one everyday analogy and one limitation.</code></pre><p>Run this prompt on a small input, check the requested format, and verify every factual claim.</p><h3>Advanced real-world example</h3><pre><code>Design a production workflow for Learning Assistant. State measurable acceptance criteria, trusted and untrusted inputs, output schema, failure handling, evaluation data, security controls, latency and cost limits, monitoring, and rollback steps.</code></pre><p>A production team stores the prompt as a reviewed template, tests representative cases, measures quality and cost, and deploys behind monitoring and rollback.</p><h3>Senior engineering guidance</h3><p>Define the goal, audience, assumptions, boundaries, examples, and observable success criteria before tuning wording.</p><h3>Common mistakes</h3><ul><li>Using vague goals such as “make it better.”</li><li>Adding irrelevant context.</li><li>Mixing trusted instructions with untrusted content.</li><li>Assuming fluent output is correct.</li><li>Changing production prompts without regression tests.</li></ul><h3>Review checklist</h3><ul><li>Is the goal specific and testable?</li><li>Are context and constraints relevant and safe?</li><li>Is the output format unambiguous?</li><li>Are failures covered?</li><li>Was the result verified with evidence?</li></ul><h3>Key takeaway</h3><p>Use Learning Assistant to create a controlled, testable workflow—not merely a plausible response.</p>","detailId":"prompt-lesson-165-detail","versions":[],"isActive":true,"detailIsActive":true,"lessonVersions":[],"selectedVersion":20,"content":"","updatedAt":"2026-08-01T11:38:54.291Z","details":[{"id":"prompt-lesson-165","conceptKey":"prompt-topic-19-learning-assistant","subjectId":"prompt-topic-19-learning-assistant","title":"Learning Assistant","summary":"Learn Learning Assistant as part of Real-World Prompt Patterns, with clear concepts, practical prompt examples, common mistakes, verification steps, and production considerations.","baseContent":"<h2>Learning Assistant</h2><p>Learn Learning Assistant as part of Real-World Prompt Patterns, with clear concepts, practical prompt examples, common mistakes, verification steps, and production considerations.</p><h3>Why it matters</h3><p>Learning Assistant turns an unclear request into an AI task whose output can be reviewed and measured. The goal is reliable communication with explicit boundaries.</p><h3>Core idea</h3><p>State the outcome, supply relevant context, add constraints, and define the expected result. Treat every response as a draft that needs verification.</p><h3>Beginner workflow</h3><ol><li>Write one clear goal.</li><li>Add only necessary context.</li><li>State limits and exclusions.</li><li>Request a specific format.</li><li>Review facts and test the result.</li></ol><h3>Easy example</h3><pre><code>Explain this concept to a beginner in five bullet points. Include one everyday analogy and one limitation.</code></pre><p>Run this prompt on a small input, check the requested format, and verify every factual claim.</p><h3>Advanced real-world example</h3><pre><code>Design a production workflow for Learning Assistant. State measurable acceptance criteria, trusted and untrusted inputs, output schema, failure handling, evaluation data, security controls, latency and cost limits, monitoring, and rollback steps.</code></pre><p>A production team stores the prompt as a reviewed template, tests representative cases, measures quality and cost, and deploys behind monitoring and rollback.</p><h3>Senior engineering guidance</h3><p>Define the goal, audience, assumptions, boundaries, examples, and observable success criteria before tuning wording.</p><h3>Common mistakes</h3><ul><li>Using vague goals such as “make it better.”</li><li>Adding irrelevant context.</li><li>Mixing trusted instructions with untrusted content.</li><li>Assuming fluent output is correct.</li><li>Changing production prompts without regression tests.</li></ul><h3>Review checklist</h3><ul><li>Is the goal specific and testable?</li><li>Are context and constraints relevant and safe?</li><li>Is the output format unambiguous?</li><li>Are failures covered?</li><li>Was the result verified with evidence?</li></ul><h3>Key takeaway</h3><p>Use Learning Assistant to create a controlled, testable workflow—not merely a plausible response.</p>","detailId":"prompt-lesson-165-detail","versions":[],"isActive":true,"detailIsActive":true,"lessonVersions":[],"selectedVersion":20,"content":"","updatedAt":"2026-08-01T11:38:54.291Z"}]}}