The RAG Hype Cycle
Everyone's building RAG. It's become the default answer to "how do I make my LLM know about my data?" And for good reason—retrieval-augmented generation genuinely works. But here's what nobody tells you: RAG is table stakes, not a moat.
I've seen dozens of AI products fail not because their RAG implementation was bad, but because they treated retrieval as the entire solution rather than one component of a larger context architecture.
The Vector Similarity Trap
Here's the pattern I see repeatedly: a team builds a beautiful vector database, embeds all their documents, and calls it a day. They've solved the "knowledge" problem, right?
Wrong.
// What most teams build
const context = await vectorDB.similaritySearch(query, k=5);
const response = await llm.generate(query, context);
This approach fails for three critical reasons:
- Semantic similarity ≠ relevance — Two chunks can be semantically similar but contextually irrelevant
- No temporal awareness — Yesterday's information might contradict today's truth
- Missing relationship context — Documents don't exist in isolation
What Context Architecture Actually Means
Context engineering isn't about finding the right chunks—it's about constructing the right mental model for the LLM. This requires:
1. Hierarchical Context Layers
Think of context like an onion. The outer layers are general knowledge, inner layers are specific to the current task:
const contextLayers = {
system: "You are a technical advisor for enterprise software...",
domain: await getDomainContext(user.industry),
session: await getConversationHistory(sessionId),
retrieval: await vectorDB.search(query),
immediate: userMessage
};
2. Context Freshness Management
Not all information ages equally. Your RAG system needs to understand temporal relevance:
- Evergreen content: Core documentation, principles
- Time-sensitive: Pricing, availability, current events
- Ephemeral: User session state, temporary context
3. Relationship Graphs
Documents reference each other. Users belong to organizations. Products have dependencies. Your context system needs to understand these relationships, not just text similarity.
The Path Forward
RAG will remain a critical component of production AI systems. But winning products will be built by teams who understand that retrieval is just the beginning.
The real work is in:
- Context orchestration — Deciding what context to include when
- Token economics — Maximizing information density per token
- Coherence maintenance — Ensuring context doesn't contradict itself
In the next article, we'll dive deep into why the context window is smaller than you think—and what to do about it.
This is part of my ongoing series on context engineering. Subscribe to get notified when new articles drop.
