Context is the real driver of LLM quality

Context windows aren't failing you; your prompting is. I was deep in a research thread the other night, expecting the model to follow my logic like a human collaborator would, only to have it suddenly pivot to explaining some random noun from five messages ago. My first thought was that the model had gone "dumb," but looking at my own input, the fault was mine. I was treating the chat window like a natural conversation where the AI reads my mind, but if the specific detail isn't explicitly on the screen, it doesn't exist to the LLM.

I've moved away from the "it's just a generic model" excuse and moved toward a much more aggressive style of prompting that actually delivers value. Here is how I've restructured my workflow to get high-utility results:

1. Killing the pronouns. I stopped using words like "it," "that," or "they" in follow-up questions. If I am discussing a specific piece of software, I name that software in every single prompt. It feels redundant to a human, but for an LLM, it's the only way to prevent hallucinations.

2. Prioritizing intent over topics. A lot of people just ask "Tell me about X." That results in a useless Wikipedia summary. I've found that if I frame my intent—for example, "I am trying to decide if X is worth the cost, give me a pros and cons list"—the model actually has something to optimize for.

3. Aggressive constraint setting. Adjectives like "detailed" or "casual" are too vague. Now, if I need a specific vibe or format, I provide a single sentence as a concrete example of the style I want. You have to give it the pattern to match.

The model is a pattern matcher, not a mind reader. If you give it a lazy, middle-of-the-road prompt, you are going to get a middle-of-the-road response. You have to provide the exact context if you want to avoid the generic sludge.