The marketing says "1 million tokens." The reality is more nuanced.
Large context windows are remarkable engineering achievements, but they don't solve the fundamental challenges of knowledge-intensive AI. In fact, they create new ones.
The Attention Dilution Problem
When you stuff 100,000 tokens into a context window, the model doesn't attend to all of it equally. Attention mechanisms have inherent biases:
Primacy bias: Early content gets disproportionate attention.
Recency bias: Recent content (especially near the query) dominates retrieval.
The "lost in the middle" effect: Content in the middle of long contexts is systematically underweighted.
This isn't a bug—it's how transformers work. And it means that simply expanding context windows doesn't guarantee better answers.
Context Engineering as a Discipline
Context engineering is the practice of deliberately structuring what goes into a context window and how it's organized:
1. Hierarchical Context Construction
Instead of flat retrieval, build context in layers:
- Level 1: Direct answers and key facts (high attention zone)
- Level 2: Supporting evidence and details
- Level 3: Background context and definitions
Position the most critical information where attention is naturally strongest.
2. Semantic Compression
Not everything needs to be in the context verbatim. Techniques include:
- Extractive summarization of supporting documents
- Entity-focused compression that preserves key relationships
- Chain-of-thought prompts that guide the model's reasoning
3. Dynamic Context Management
Static retrieval isn't enough. Effective systems:
- Rerank after initial retrieval based on query analysis
- Prune irrelevant content before context assembly
- Augment with synthesized context when gaps are detected
Multi-Agent Context Optimization
This is where it gets interesting. Instead of one monolithic retrieval, use specialized agents:
- Query analysis agent: Understands intent, entities, and requirements
- Retrieval agents: Each optimized for different content types
- Synthesis agent: Combines and structures retrieved content
- Validation agent: Checks for gaps, conflicts, and quality
These agents can operate in parallel, dramatically improving both speed and quality.
Measuring What Matters
You can't improve context engineering without measurement:
- Attribution accuracy: Can you trace answers to sources?
- Completeness: Are all relevant facts included?
- Precision: Is there irrelevant noise in the context?
- Latency: What's the cost of better context?
The Bottom Line
Context windows will keep growing. But window size is a ceiling, not a floor. The quality of your answers depends on what you put in that window and how you structure it.
Context engineering is the new frontier of RAG optimization.