Scaling LLMs to Larger Codebases

Burn through your token limit in agent mode just to thrash around a few more times trying to identify where the agent "misunderstood" the prompt.

The only time LLM's work as coding agents for me is tightly scoped prompts with a small isolated context.

Just throwing an entire codebase into an LLM in an agentic loop seems like a fools errand.

Better than that, ask the LLM. Better than that, have the LLM ask itself. You do still have make sure it doesn't go off the rails, but the LLM itself wrote this to help answer the question:

### Pattern 10: Student Pattern (Fresh Eyes)

*Concept:* Have a sub-agent read documentation/code/prompts "as a newcomer" to find gaps, contradictions, and confusion points that experts miss.

*Why it works:* Developers write with implicit knowledge they don't realize is missing. A "student" perspective catches assumptions, undefined terms, and inconsistencies.

*Example prompt:* ``` Task: "Student Pattern Review

Pretend you are a NEW AI agent who has never seen this codebase. Read these docs as if encountering them for the first time: 1. CLAUDE.md 2. SUB_AGENT_QUICK_START.md

Then answer from a fresh perspective:

## Confusion Points - What was confusing or unclear on first read? - What terms are used without explanation?

## Contradictions - Where do docs disagree with each other? - What's inconsistent?

## Missing Information - What would a new agent need to know that isn't covered?

## Recommendations - Concrete edits to improve clarity

Be honest and critical. Include file:line references." ```

*Uses cases:* Before finalizing new documentation, evaluating prompts for future Agents.

This is a good article, but misses one of the most important advances this year - the agentic loop.

There are always going to be limits to how much code a model can one-shot. Give it the ability to verify its changes and iterate, massively increase its ability to write sizeable chunks of verified and working code.