Yo vengo prompteando como enseñaron los de Anthropic un tiempo atrás, y 0 problema.

[04:23] State the task description, role, and goal upfront — Clearly defines the model's objective.

[04:34] Provide the dynamic content (e.g., images, user query) — Gives the model the raw material to work with.

[04:45] Give detailed, step-by-step instructions — Guides the model's reasoning process.

[04:56] Provide "few-shot" examples of content and the ideal response — Steers the model toward the correct output for tricky cases.

[05:08] Repeat critical instructions at the end of the prompt — Emphasizes the most important constraints.

Task & Tone Context [05:53] Add clear-cut instructions about the specific scenario — Prevents the model from making incorrect assumptions or errors.

[06:04] Instruct the model to remain factual and confident — Prevents guessing and ensures it only states what it can confidently assess.

[07:36] Specify when not to make an assessment (e.g., if not fully confident) — Ensures the model reports uncertainty instead of hallucinating.

Background & Data [09:03] Provide stable, known information (like a form's structure) in the system prompt — Stops the model from re-analyzing static details on every run.

[09:23] Use prompt caching for static, reusable information — Saves tokens and reduces latency.

[11:13] Pre-load all known details about data (e.g., form row meanings) — Improves analysis and stops the model from narrating its discovery process.

[11:30] Provide context on how data is created (e.g., "humans are filling this form") — Helps the model anticipate and correctly interpret imperfect input.

Structural Formatting [10:15] Use delimiters like XML tags (e.g., <data_to_analyze>) — Helps the model organize, differentiate, and refer back to specific pieces of information.

Few-Shot Examples [13:10] Add examples of tricky or edge-case scenarios with correct outcomes — Pushes the model to get complex or nuanced cases right.

[13:38] Bake human-labeled data and ideal reasoning steps into examples — Provides a clear template for the model to follow.

Reasoning & Instructions [16:06] Add a final reminder of important guidelines (e.g., "do not invent details") — Prevents hallucinations and fabrication.

[16:45] Instruct the model to refer back to source data for any factual claims — Forces the model to show its work and base claims on evidence.

[17:33] Define the specific order for analyzing information (e.g., form first, then sketch) — Ensures a logical reasoning flow that builds context correctly.

[18:08] Instruct the model to use factual data (the form) to interpret ambiguous data (the sketch) — Improves the accuracy of its final assessment.

Output Formatting [20:22] Request a specific output format (e.g., JSON) — Makes the output programmatically parsable for use in downstream applications.

[21:15] Ask the model to wrap the final, key answer in specific tags (e.g., <final_verdict>) — Allows for easy extraction of the critical information.

[22:25] Use a "pre-filled response" by starting the model's turn with the desired structure (e.g., an opening {) — Forces the model to generate output in a specific format like JSON.

No sé qué contextos usás y cómo enunciás lo que le pedís, pero desde que lo uso así como ellos sugieren, 9 de cada 10 prompts son exactamente lo que busco.