Prompt As Intent Transmission

Prompt as intent transmission is Li Jigang / 李继刚’s broader view of prompting in E45 孟岩对话李继刚：人何以自处. A prompt is not only a carefully worded chat message. It can include a role instruction, a PDF, local notes, prior conversations, a memory file, a task folder, a personal principle, or any other medium that lets a model receive the user’s intention.

This reframes prompting away from tricks. The important question is whether the model can understand what the person wants, what frame it should reason from, what context matters, and what shape of answer is acceptable. That makes prompting a branch of AI Communication Ability and Context Engineering rather than a separate craft of magic phrases.

EP 15: Unveiling Data Scientist’s Role in the Generative AI Era adds a data-science career version through Marina. Her example that changing one word in a prompt can change relevance supports the same broader point: prompt quality depends on task understanding, domain context, and Data Scientist Generative AI Fluency, not only a reusable wording template.

EP 17: AI’s Impact on Creativity: A Consumer’s Perspective adds a consumer and volunteer-work version through Mark. His speech, image, song, and Google Apps Script examples show prompting as ordinary task communication: the user supplies topic, audience, event, workflow, and constraints so ChatGPT, DALL-E, or Suno can produce a useful starting point.

The episode also warns that good prompts are difficult because parameters and judgment matter. Li compares prompt writing to investment valuation: formulas are easy to name, but the parameter choices and mental path depend on accumulated taste, context, and discernment.

Key Claims

• Prompting is the act of transmitting intention into model space.
• Documents, files, notes, memory, and local context can be prompt material.
• Good prompting depends on self-knowledge and task knowledge, not only templates.
• In everyday creative work, prompt quality depends on audience, tone, event context, tool choice, and the user’s willingness to revise the output.
• In applied data-science work, a prompt is useful only when the data scientist can judge whether the answer fits the business target.
• Meta-prompts can produce artificial-sounding outputs if there is not enough rich context behind them.
• High-density conversation can create the context from which better prompts later emerge.

Connections

• AMV Prompt Framework — Li’s compact framework for specifying start, direction, and mental path.
• AI Communication Ability — prompts are a communication skill.
• Context Engineering and Persistent Agent Memory — context and memory widen what a prompt can carry.
• Human Agency Under AI — the user’s intent still has to be supplied before the model can help.
• Output Quality Gates — acceptance standards are part of transmitting intent.
• AI Skills — reusable prompts can become packaged procedures for agents.
• Data Scientist Generative AI Fluency, Domain Expert Alignment, and Marina (Data Science With Sam) - EP15’s applied data-science prompting branch.
• Mark (Data Science With Sam), AI Creative Collaboration, AI First-Draft Generation, AI Assisted Light Coding, and AI Professional Data Security - EP17’s consumer, volunteer, and workplace prompting branch.