LLM Context Window Management: RAG vs Compression vs Full Context
When to use RAG, when to summarize conversation history, and when a 1M token context window is actually worth the cost. A practical decision guide for developers.
Context window limits determine what your LLM can "see" in a single request. In 2026, frontier models offer 128k to 2M token windows — but larger context means higher cost per request. This guide explains how to manage context strategically: what to include, what to compress, and when to reach for a long-context model vs a RAG approach.
Context Window Sizes in 2026
"Cost for Full Context" shows what a single request costs if you fill the entire context window. Filling a 1M token window with Claude Sonnet costs $3.00 — for input alone, before output tokens. At scale, context management is essential.
The Three Strategies for Long Context
Strategy 1: RAG (Retrieval-Augmented Generation)
RAG is the most cost-effective approach for knowledge-base applications. Instead of inserting an entire document corpus into the prompt, you embed documents as vectors and retrieve only the top-k relevant chunks at query time. A knowledge base with 10,000 pages becomes a 2,000-token prompt instead of a 5,000,000-token prompt.
RAG limitation: it fails when the answer requires synthesizing information spread across many non-contiguous parts of a large document. For those cases, use full-context models.
Strategy 2: Context Compression
For long conversations or multi-step agent workflows, context grows with every turn. Two compression techniques manage this without losing important information:
Sliding Window
Keep only the last N turns in full detail. Discard turns older than the window. Works well for customer support (recent context is most relevant) but loses historical context that may matter for complex tasks.
Progressive Summarization
When the conversation exceeds a token threshold, call the model to summarize the oldest N turns into a compact summary, then replace those turns with the summary. A 20-turn conversation that would fill 8,000 tokens becomes a 300-token summary plus the recent 5 turns.
Strategy 3: When to Use Full Long-Context Models
Some tasks genuinely require the full context. The following cases justify a long-context model over RAG:
Prompt Caching: The Best Tool for Repeated Long Context
When the same long context is sent repeatedly — same system prompt, same document, same codebase — prompt caching reduces the cost of the repeated portion by 50–90% depending on provider.
Structure your prompts to put the static context (system prompt, documents) at the beginning and variable content (user query) at the end. The cached prefix must be identical across requests — even a single token difference invalidates the cache.
Bottom Line
Default to RAG for knowledge-base applications — it's 10–100x cheaper than full context. Use progressive summarization for long conversations. Reserve full long-context models for tasks where cross-document relationships genuinely matter. And always enable prompt caching on the static prefix of your prompts — it's the single highest-leverage cost reduction for repeated context.
Related: Prompt Caching Guide · Token Optimization: 7 Techniques · Cost Routing Tool →