Front-Load Context Instead of Long Conversations

300,000 tokens upfront.

Two turns.

One answer.

That is the math that makes million-token context windows useful.

The drift problem

You have a million tokens available. You use them like a conversation: ask a question, get an answer, ask a follow-up, get another answer. Fifteen turns later, the model is referencing something from turn three that no longer matches your codebase. You correct it. It apologizes. It drifts again.

The context window is not the problem. The turn count is.

Million-token models do not perform well over 15 to 20 turns. Each turn introduces new information that can conflict with earlier context. The model has to reconcile what you said in turn four with what you said in turn twelve. By turn eighteen, you are debugging the conversation instead of debugging the code.

The front-load pattern

The teams getting reliable results from large context windows use a different pattern: dump everything upfront, then ask one focused question.

"Basically, give it your 20,000 system prompt, 780,000 worth of code base or documentation, and then do your ask and you got 200,000 to finish the job."

Hannes Rudolph, Office Hours S01E12

The ratio matters: 80% context, 20% generation headroom. You front-load the codebase, the documentation, the relevant files. Then you ask. The model has everything it needs in one pass.

This pattern works especially well for specific task types:

Translation tasks: Reading 17 or 20 files at once, then doing one or two writes. The model sees all the source files before it generates any output.

Codebase analysis: Loading the entire relevant module, then asking one diagnostic question. No back-and-forth about which files to read.

Library generation from documentation: Feeding the full API reference upfront, then requesting the wrapper code in a single generation pass.

"I find the million models to be quite effective when I feed them a huge chunk of context up front. For example, when we're doing translating files, doing, you know, 17 or 20 reads at once, boom, filling it up and then doing one or two writes with concurrent, right? It does. It works pretty smoothly."

Hannes Rudolph, Office Hours S01E12

The tradeoff

Front-loading requires knowing what context matters before you start. If you load 600,000 tokens of the wrong files, you have wasted the window. The pattern assumes you can identify the relevant context upfront.

For exploratory work, where you do not know which files matter yet, you may need a few turns to narrow scope. But once you know the scope, switch to front-load mode. Stop iterating and start dumping.

The other constraint: once the conversation drifts, recovery is expensive.

"It works good until it doesn't and then once it doesn't it's hard to get it back on track."

Hannes Rudolph, Office Hours S01E12

If turn twelve goes wrong, you often cannot fix it in turn thirteen. The model has internalized assumptions from the earlier turns. Starting fresh with a front-loaded context is usually faster than trying to correct course mid-conversation.

Why this matters for your team

For a team running translation or migration projects, the front-load pattern changes throughput. Instead of a developer babysitting a 20-turn conversation, they set up the context once and let the model complete the task. The time spent on conversation management drops to near zero.

For codebase analysis, the same shift applies. Loading the full module upfront means the model can answer diagnostic questions without asking "which file is that in?" five times. One context load, one answer.

The compounding effect: if three developers on a five-person team each save 30 minutes per day by eliminating conversation drift, that is 7.5 hours per week. Enough to ship one more feature per sprint.

The heuristic

Give the model 300,000 to 600,000 tokens of context in the first message. Limit yourself to two or three turns. If the conversation drifts, start fresh with a new front-loaded context instead of trying to correct.

The million-token window is not for long conversations. It is for short conversations with long context.

How Roo Code optimizes context loading

Roo Code closes the loop on context management by automatically gathering relevant files, running commands, and iterating based on results. Instead of manually curating which files to include, Roo Code can traverse your codebase and load the context it needs for a given task.

With BYOK (Bring Your Own Key), you control which models you use and how many tokens you spend. When working with million-token models, Roo Code's ability to read multiple files in parallel and execute iterative workflows means you can front-load context without manually copying file contents into a chat window.

The key advantage: Roo Code treats each task as a focused unit of work. You describe the outcome, and the agent gathers context, proposes changes, runs tests, and iterates - typically completing in two to three turns rather than 15 or 20.

Comparison: conversational vs. front-loaded context