Context as Dynamic Currency, Not Static Retrieval

In production AI systems, context is the primary constraint on value delivery. While most teams focus on embedding quality and similarity search, the real challenge lies in treating context as a limited, dynamic resource that must be allocated strategically. Simply retrieving semantically similar chunks and dumping them into the prompt creates a fundamental mismatch—LLMs don't process context uniformly. Attention mechanisms distribute cognitive load across all tokens, and as context grows, each piece receives proportionally less focus. This isn't a simple scaling problem; it's an architectural one where RAG performance plateaus or degrades despite adding more "relevant" information.

Information Selection Through Relevance Cascading

Effective context engineering requires multi-stage filtering beyond vector similarity. A relevance cascade combines semantic search with domain-specific filters and reranking strategies to surface truly useful information before it enters the context window.

Relevance Cascade Pipeline:

1. Stage 1: Semantic similarity search retrieves top 50 candidates using embeddings
2. Stage 2: Domain filters apply business logic (recency, user permissions, document type)
3. Stage 3: Reranking model scores candidates based on query-document interaction
4. Stage 4: Diversity sampling prevents redundant information from consuming tokens

This pipeline reduces attention dilution by ensuring only high-signal chunks reach the model, rather than relying on the LLM to filter noise during generation.

Hierarchical Organization Mirrors Expert Reasoning

How you structure context matters as much as what you include. Expert reasoning follows patterns: start with the domain object, then expand into fields and relationships. Present a "Customer" entity with its core attributes before diving into transaction history. This hierarchical organization aligns with how LLMs process information—early context tokens receive more attention and set the frame for interpreting subsequent details. Unstructured context forces the model to reconstruct relationships mid-generation, wasting tokens and cognitive capacity on pattern matching instead of reasoning.

Applied Insight: Evolve Context, Don't Accumulate It

Blindly appending conversation history creates exponential context bloat. Instead, implement context evolution strategies: summarize previous turns, extract key entities and decisions, and rebuild context windows dynamically based on the current query. For multi-turn applications, maintain a structured context state that tracks what the model needs to remember versus what can be safely compressed or discarded. This approach keeps context windows lean and attention focused on high-value information.