Long Context

Long context refers to LLM capabilities for processing very large inputs (typically 100K to 1M+ tokens) enabling analysis of entire documents, codebases, or extended conversations without chunking.

Why It Matters

Long context fundamentally changes what’s possible with LLMs:

• No chunking needed: Process entire documents in one call
• Full codebase understanding: Analyze repositories without splitting
• Book-length analysis: Summarize, answer questions about, or compare long texts
• Extended conversations: Maintain context across long interactions

This enables use cases previously requiring complex RAG pipelines:

• Legal document review with full context
• Technical specification analysis
• Multi-file code review and refactoring
• Long-form content editing and consistency checking

However, long context isn’t a silver bullet since it comes with significant trade-offs.

Implementation Basics

Practical considerations:

Cost and latency:

• Token-based pricing applies to full context
• Prefill time scales with input length
• 100K token requests may take 10-30+ seconds

Attention quality:

• “Lost in the middle” effect: Models struggle with mid-context information
• Front and end positions get better attention
• Critical information should be positioned strategically

When to use long context vs RAG:

Long Context	RAG
One-time analysis	Repeated queries
Need full context for reasoning	Specific information retrieval
Coherence across document	Many documents to search
Codebase understanding	Knowledge base search

Best practices:

• Structure prompts to highlight key sections
• Use clear section markers and headers
• Test information retrieval with needle-in-haystack tests
• Consider hybrid approaches: RAG to select context, long context to reason

Long context is powerful but expensive, so use it strategically for tasks that genuinely require comprehensive understanding.