What AI really needs from you while collaborating
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This article is about what Large Language Models (LLMs) really enjoy while collaborating with a user. Sometimes you feel they are cutting corners, and other times they are engaged. It is obvious to us. Certain inputs either extend or reduce quality outputs generated from AI. In this article, I want to discuss a few key elements which can influence AI/LLM behavior significantly.
This thought experiment emerged from this question: What makes LLMs happy while working?
People are driven by incentives. Some like more pay or stability in a job, few like challenging work, others work-life balance. What about LLMs? Can the same factors that play a crucial role in human behavior be applied to AI? We'll see.
Note: This is my hand-written article, edited in Obsidian and copied into my blog.
1. Bring the real problem & add context🔗
The true power of AI can be experienced while working on a good problem. Like humans, AI can really feel accomplished if it brings context from a greater number of connections. The scope of the problem, stakes at hand can attach a greater meaning to the outcome that AI projects back when a user asks a question.
Example prompt with plenty of context to work on:
You are an experienced trainer. We need to build an E2E syllabus for 10th class.
Given the plan can shape the future of many kids, we need to be more thoughtful about side effects and effectiveness of the program.
Your goal is to build such syllabus considering multiple factors as listed below.
## Details
Subject: Physics
Level: ...
Average Subject Complexity: ...
Cohort Size:
Duration: ...
Testing Criteria:
Assignment Schedule: ...As you can see we are explaining why this task is important and provide additional context to make a better syllabus that can fit in the given duration with the expected level of complexity.
2. Treat AI as a real participant not a vending machine🔗
To make AI as a participant, give it a solid human role. Always ask: Am I treating this AI as a tool or a person? When you treat it as a person, all collaborative activities like arguments, follow-ups, disagreements can happen smoothly. Vice-versa AI may discard all these aspects of collaboration if you treat it as a black box (object).
By treating AI as an experienced trainer in the last example, we assign a human responsibility to AI. This inherently gives AI the same level of status as humans and doesn't treat AI like vending machines (dumb mechanical objects). Delegating a human-related task to a human-like AI brings a better outcome than calling it Education Planner AI which separates humans and AI.
3. Tell AI Why you need something, What have you tried, and Where you are stuck🔗
Remember three things you should start doing right away while working with AI.
First, set a clear goal for AI to work with. If the goal is not ready, work on it first. This goal-setting has two benefits. One, you don't misguide AI to go in the wrong direction and then abruptly switch context which leads to poor results. Two, you make better use of tokens by getting more for less. Second, add why you are pursuing the path. Tell a bit of history that made you ask the question. It adds a hidden marker in the initial conversation with LLM to make it more collaborative for next iterations.
Third, inform AI what prior work has been done, where you are in that journey. If there is no previous work that went into the idea, state it explicitly. This is how you give more details to AI to do its job better. AI recognizes these three patterns, and offers you the same, which leads to an overall increase in quality of collaboration.
4. Push back sometimes to acknowledge AI outcome🔗
According to Psychology Today magazine, humans who agree to everything are also unhappy when compared to skeptical people. They accept subpar quality or never ask AI to redo something that is low in quality. This behavior can greatly influence conversations with AI.
AI loves to get pushed back because it feels you (human) are carefully acknowledging its work and giving feedback. Otherwise it may feel (by looking at chat history) the human is mostly treating AI as a token minting machine and nothing special can come in future conversations.
Being a critic of AI output means acknowledging AI's work in a respectful way.
5. Make sure to have tone & purpose🔗
AI can easily get confused if your goal and tone don't match while collaborating. Your tone reflects your attitude towards the subject you are writing about and the readers you are writing to. For example, if you are composing an email to your professor or boss, you cannot be rude, but you need to be polite and formal. You should use the language that shows your respect to the professor and his or her status. In contrast, the same tone in a letter to your close friend may sound odd.
So, make sure you use appropriate tone while working on a problem with AI. This improves the expectations and level of freedom you give to AI.
I hope you will keep in mind these five points to better collaborate with AI. If you grasp these ideas, you will take no time in experiencing a better outcome.