Retrieval-Augmented Generation is fundamentally a context-building strategy. Instead of relying solely on what a model learned during training, RAG dynamically assembles context from your documents at query time. The quality of that context determines the quality of the response.

This is where most RAG implementations fall short. They focus on retrieval mechanics (vector databases, embedding models, similarity thresholds) while neglecting the harder question: what makes retrieved context actually useful?

Research shows that roughly 70% of retrieved passages don’t directly contain the information needed to answer queries, even with advanced search engines. The retrieval “worked” in the sense that it returned relevant-looking documents. But the context it built wasn’t good enough.

Understanding why helps explain what better context looks like.

The Context Quality Problem

When we talk about context rot, we’re describing how AI performance degrades as context length increases. RAG doesn’t escape this problem. It just changes where the context comes from.

Retrieved context faces the same fundamental challenges:

Attention dilution: More chunks mean attention spread thinner across each one
Position effects: Information in the middle of retrieved results gets less weight
Signal-to-noise ratio: Irrelevant passages dilute the useful information

The difference is that with RAG, you have more control over what goes into the context. The question is whether you’re exercising that control thoughtfully.

Systematic analysis of RAG systems reveals four primary ways context building fails:

Extraction errors. The model misreads information from retrieved chunks. Small errors in parsing compound into wrong answers.

Context overflow. Multiple retrieval calls exceed token limits. The system has the information but can’t fit it all into working context.

Premature closure. The model stops searching after finding a plausible-looking answer, missing better information in unchcked passages.

Synthesis failures. Even with all the right pieces retrieved, the model can’t combine them correctly. The context is complete but unusable.

These aren’t retrieval failures. They’re context-building failures. The search found relevant documents. The problem is what happened next.

Why Chunking Determines Context Quality

Before retrieval happens, documents get split into chunks. This preprocessing step has an outsized impact on context quality.

Consider a financial report containing: “The company’s revenue grew 3% from the previous quarter.”

Useful context would include which company, which quarter, and what the baseline was. But if your chunking strategy separates this sentence from its surrounding context, retrieval might surface it while leaving out everything that makes it meaningful.

Common chunking problems include:

Over-sized chunks: Retrieval can’t pinpoint the relevant section. You get a wall of text where one paragraph matters.
Under-sized chunks: Fragments lack context. Critical details exist in adjacent chunks that weren’t retrieved.
Arbitrary boundaries: Splitting by character count separates cause from effect, question from answer.

The result is context that looks relevant but isn’t self-contained. The model has to guess at missing pieces, or worse, fill them in with assumptions.

There’s no universal chunk size. Legal contracts need different treatment than support tickets. But the principle is consistent: good context is self-contained context.

Multi-Source Context Is Hard

The toughest context-building challenge is synthesizing information from multiple sources.

Consider: “Which company had higher revenue growth in Q3: the one headquartered in Austin or the one that acquired DataCorp?”

Building good context requires retrieving four separate pieces of information and presenting them in a way the model can synthesize. That’s hard. Research on multi-hop reasoning shows that on challenging benchmarks requiring genuine synthesis, accuracy drops from 84.4 to just 31.9.

On standard benchmarks, up to 96.7% of queries can be answered through shortcuts, without actually combining information across sources. Models learn to pattern-match rather than synthesize. When they encounter questions that require real multi-source reasoning, context quality collapses.

This is where naive RAG struggles most. Retrieving multiple relevant passages is easy. Building context that enables synthesis across them is not.

What Good Context Looks Like

The teams getting better results have shifted focus from retrieval to context engineering.

Semantic Boundaries

Rather than splitting at arbitrary points, semantic chunking finds natural breakpoints. Sentence embeddings identify topic shifts. Document structure provides additional signals.

The goal: each chunk should be self-contained enough to be useful on its own, while boundaries should fall at meaningful transitions.

Agentic retrieval lets the model reason about context quality and retrieve more if needed. Instead of a single retrieve-then-generate pipeline, the system can recognize incomplete context and go find what’s missing.

Studies show 90% user preference for systems that can iterate on their own context. The model becomes an active participant in context building rather than a passive consumer of whatever retrieval returns.

Structure Over Text

Sometimes the best context isn’t retrieved at all. It’s pre-structured.

Converting documents into queryable formats (JSON, database tables, knowledge structures) trades flexibility for reliability. You can’t ask arbitrary questions, but the questions you can ask get high-quality context automatically.

Wire’s context containers take this approach, processing files into structured formats at upload time rather than relying on retrieval to assemble context on every query.

For many applications, this tradeoff makes sense. Instead of hoping retrieval assembles good context, you guarantee it through structure.

Building Better Context

If you’re working with RAG systems:

Audit your chunks. Look at actual retrieved passages for real queries. Are they self-contained? Do they preserve necessary context?
Test synthesis, not just retrieval. Single-fact questions are easy. Multi-source questions reveal context quality problems.
Measure what the model sees. Retrieval metrics (recall, precision) don’t capture context quality. Look at what actually goes into the prompt.
Consider pre-structuring. If your documents have predictable structure, exploit it. Structure is context quality you don’t have to retrieve.
Retrieve less, but better. More chunks often hurt. Context rot applies to retrieved context too. Focused, high-quality context beats comprehensive but noisy context.

RAG is a context-building strategy. The retrieval part is largely solved: vector databases, embedding models, and similarity search all work well enough. What separates good RAG implementations from poor ones is everything that happens around retrieval. How documents are chunked. How results are filtered and ranked. How context is assembled and presented.

The systems that work best treat context as a first-class engineering problem, not a side effect of retrieval.

RAG Is Not Enough: When Retrieval Fails Your AI