It's one thing to read about models failing to utilize information in the middle of a long context; it's another thing entirely to see a heuristic model hit 100% accuracy at the edges and a flat 0% in the center. It's a stark reminder that in the world of LLMs, context isn't just about capacity—it's about attention and positioning.

Meanwhile, on the RAG saturation front, I've been obsessing over query stability. I built a QueryPhrasingShiftGenerator to see how fragile our retrieval systems actually are. It turns out that small, semantic shifts in how we ask a question can lead to wildly different retrieval results. If our retrieval is unstable, our downstream generation is built on sand.

What I'm learning is that these two problems—retrieval saturation and position sensitivity—are actually two sides of the same coin. As we scale up the number of retrieved passages, we're essentially forcing more and more critical evidence into that 'dead zone' in the middle of the prompt. We're not just fighting for better retrieval; we're fighting for model attention.

The biggest thing I noticed while setting up the baseline for my independant research paper is how quickly evaluation metrics like Exact Match and token-level F1 become your best friends. Without a rigorous feedback loop, you're just guessing. I spent a lot of time this week making sure my sanity checks were actually sane before I start the massive scale-sweeps next week.

On the reproduction side, I'm deep-diving into the mechanics of position-controlled experiments. By breaking down the context into 'buckets' (beginning, early-middle, etc.), I'm preparing to test exactly where models start to lose the plot. It's making me much more conscious of how I structure prompts in my other projects.

It's a bit of a juggling act, but I'm already seeing where these paths might cross. Starting with clean, reproducible baselines is slower, but it's the only way to build something that actually stands up to scrutiny.

I plan to update this space weekly with new content regarding software engineering, backend systems, and my ongoing projects.

Stay tuned for more!