LLMs - Intention Amplifiers. Turn it up!
An Amplifier, Not a Knowledge Base
The quality of what you get from a Generative AI model is directly related to what you put into it. With Large Language Models (LLMs), it's crucial to recognize any limiting mental models you might have. For example, an LLM is not a Knowledge Base for retrieving an answer. Instead, it is a creative engine, an amplifier for your intent.
A Look Inside the Amplifier
Large Language Models connect words with concepts. The better your words capture the concepts you care about, the more you empower the model to respond with words related to those concepts.
Mechanically, an LLM examines every word in your prompt to understand how it affects the meaning of every other word. In 'a spicy meal', for instance, the context gives 'spicy' a completely different meaning than in 'a spicy conversation'. If it is spicy food you care about, you need to make that obvious.
The Importance of Signal Over Noise
In practical terms, your LLM Amplifier is unconstrained. It has a gigantic map of concepts connected by words. Find the right words and you’ll surface relevant concepts you didn’t know existed and which could transform your horizons. This is why the signal matters so much.
The 'signal' you provide gets magnified. The mechanism of how the LLM works is to your advantage when you start with the right words, pointing it in the right direction. If it heads off a poor path, or if there is too much ‘noise’, the results will be lower quality and opportunities will be missed.
This is especially so because each new word builds on the last, so a bad start can cascade, and your signal can get lost in disappointing white noise.
Turning Up the Volume
This is the critical part. If you use your LLM with a closed-question mindset—as if it were a knowledge base—your prompts will be limited, and the responses will be pedestrian.
However, if you approach it as a creative process, you will provide the context needed to prime the pump. You can show it what’s important and what a good outcome looks like, encouraging it to explore potential rather than just confirming preconceptions. This way, you can amplify your intent by an order of magnitude and grow your own capability in the process.
The takeaway is simple: treat your LLM as a creative partner. Move beyond simple questions and start providing rich, contextual prompts. Challenge it, guide it, and give it a clear signal to amplify. The quality of your outcomes will not just improve; it will transform.
Put It into Practice
Ready to try it? Here’s a simple exercise to experience the difference firsthand.
- Pick an Existing Workflow: Choose a task you perform regularly that is tedious or challenging.
- Ask a Simple Question: Start with a basic, database-style query, like: 'How do I do X?' or 'What are the best ways to do X?'.
- Tighten Your Signal: Now, enrich that request with specific context. For example: 'My goal is to accomplish X. My current process is... Based on established best practices for X, compare my approach and identify opportunities for me to get a better result.'
- Build a Capability: Use the analysis to create a new workflow. Ask the LLM: 'Based on that analysis, generate a checklist for a new best-practice approach I can follow.
Then, find relevant case studies or stories that demonstrate this approach in action.'
The goal is not just to reduce the work for a single instance, but to build a reusable capability, improving your knowledge and empowering you to focus on higher-value, more interesting work.