Some AI practitioners are drowning their models in data, mistaking context window size for context quality.
As context windows expand to accommodate millions of tokens, there's a dangerous assumption at work: if the model can handle more information, it should.
Good ol' Merriam-Webster's definition of context reveals the flaw in this thinking, noting that context is: "the parts of a discourse that surround a word or passage and can throw light on its meaning."
Context throws light on meaning. Dumping everything you can find into a context window doesn't illuminate – it obscures. Like trying to spotlight a performer by flooding the entire theater with light, too much information washes out what matters most.
This is where domain expertise becomes your secret weapon. Someone who truly understands the task and data can separate signal from noise – a skill that becomes more valuable, not less, as AI capabilities grow.
Four Ways to Sharpen Your Context
1. Audit your data sources ruthlessly. Multiple sources often mean multiple versions of the same information. Find the overlaps, pick the highest-quality source, and cut the rest. Your model will thank you with clearer outputs.
2. Extract, don't summarize. Summarization steamrolls your data – where you had richness and specificity, you get generic echoes. Instead, use traditional extraction techniques or targeted LLM queries to pull exactly what you need, preserving the details and interesting bits that matter.
3. Transform strategically. This isn't about compression for compression's sake. Use your understanding of the data to preserve what makes it most valuable while eliminating what doesn't serve your specific purpose.
4. Start from first principles. Identify your most critical data source and test it alone. Then add other elements back piece by piece, comparing outputs at each step. Often, you'll discover that less data produces better results – higher fidelity through focused attention.
The irony of infinite context windows is that they tempt us to abandon the very judgment that makes us valuable partners to AI systems. Don't just fill the space because you can. Make every token count.