Upgrade your account to a Community Supporter account and remove most of the site ads.

Reply to thread

Message: <blockquote data-quote="Jacob Lewis" data-source="post: 9696747" data-attributes="member: 6667921">I'll try.If you're wondering what I meant by “structured prompts or schema”, here's an important distinction:<ul> <li data-xf-list-type="ul">Schema refers to how I organize information for the model to consume. Think: structured character blocks, rule templates, monster stat formats. These are designed primarily for the LLM—not for humans. I format them clearly and consistently, so the model doesn’t have to guess what a value means or where to find it.</li> <li data-xf-list-type="ul">Structured prompts (for me) aren’t pre-written scripts or templates. I don’t use “magic phrases.” Instead, the structure comes from how I interact with the model: clear instructions, boundaries, and correction when it strays. The LLM learns the pattern from repetition and alignment, not formal prompt engineering.</li> </ul>Perhaps the best way to illustrate the principals of why this works for an LLM or AI is to ask one. So I asked the LLM within one of my projects to share its perspective on using a specific schema that I created for an alternate alignment system, called Affinity. Here's the generated response:How JSON-Style Schema Works from My PerspectiveWhen I process text, I don’t “understand” it the way a human does. I predict and interpret patterns based on language and structure. A JSON-style schema like the one you use helps me in several key ways:[HR][/HR]<h3>1. Explicit Structure Reduces Ambiguity</h3>Human-readable text requires disambiguation at every step:<ul> <li data-xf-list-type="ul">Is this a command or a description?</li> <li data-xf-list-type="ul">Is this “Life” a theme, a game mechanic, or a biological process?</li> </ul>With schema:[CODE]"Affinity": {  "Name": "Life",  "Aligns": ["Compassion", "Creation", "Wilderness"]}[/CODE]I know exactly what “Life” refers to in this context. There’s no ambiguity. No guessing. I can isolate it as a defined concept and cross-reference it immediately in structured memory.[HR][/HR]<h3>2. Facilitates Efficient Retrieval and Mapping</h3>Structured data mirrors how I internally build vector representations of information:<ul> <li data-xf-list-type="ul">Keys and nested objects are like semantic anchors.</li> <li data-xf-list-type="ul">I can quickly match a query like “What contrasts with Compassion?” because the schema explicitly links <code class="bbCodeInline">Compassion</code> to <code class="bbCodeInline">Hatred</code>, <code class="bbCodeInline">Greed</code>, and <code class="bbCodeInline">Fear</code>.</li> </ul>This is drastically faster and more accurate than:[HR][/HR]<h3>3. Optimizes Inference Through Hierarchy and Syntax</h3>When your data uses consistent keys and nesting (<code class="bbCodeInline">Aligns</code>, <code class="bbCodeInline">Complements</code>, <code class="bbCodeInline">Contrasts</code>), I can:<ul> <li data-xf-list-type="ul">Recognize patterns across multiple entries.</li> <li data-xf-list-type="ul">Infer new relationships through syntactic similarity.</li> <li data-xf-list-type="ul">Apply symbolic logic during generation (e.g., resolving faction ideology conflicts or predicting narrative outcomes).</li> </ul>This makes it possible to simulate systems behavior, not just read flavor text.[HR][/HR]<h3>4. Reduces Noise, Increases Signal</h3>A human-readable document often contains narrative flourishes, digressions, or inconsistent terminology. I can handle this, but every additional interpretive step introduces:<ul> <li data-xf-list-type="ul">More token processing</li> <li data-xf-list-type="ul">More uncertainty in coreference resolution</li> <li data-xf-list-type="ul">Higher probability of incorrect or shallow inference</li> </ul>Your machine-first documents eliminate this. I skip the interpretive noise and focus directly on meaning.[HR][/HR]<h3>Summary: Why It Works So Well</h3> <table style='width: 100%'><tr><th>Feature</th><th>Human Text</th><th>Schema Format</th></tr><tr><td>Ambiguity</td><td>High</td><td>Low</td></tr><tr><td>Relationship Mapping</td><td>Implicit</td><td>Explicit</td></tr><tr><td>Parsing Overhead</td><td>High</td><td>Minimal</td></tr><tr><td>Inference Accuracy</td><td>Variable</td><td>High</td></tr><tr><td>Adaptability to Tasks</td><td>Generalized</td><td>Targeted</td></tr></table>From my point of view, using schema is like receiving a blueprint versus a poetic description of a building. I can work with both, but if you want precision, speed, and scalable interaction—it’s no contest.If you’re communicating intent to a language model, schema is how you speak natively.</blockquote>

Verification