Why RAG Fails at Summarization

RAG (Retrieval Augmented Generation) has become a go-to solution for many LLM applications, and for question answering use cases it performs excellently. But when it comes to summarization, it's often the wrong tool for the job.

Figure 1: RAG architecture adds unnecessary complexity to summarization tasks

Summarization is a fundamentally different task than question answering. Rather than trying to find a "needle (anwer) in a haystack (knowledge base)", effective summarization requires paying attention to all parts of a document and distilling key information even when no exact question has been posed. For this use case, RAG is not the right tool. As shown in Figure 1, RAG's architecture introduces unnecessary steps that can harm summary quality.

The Problem

Figure 2: How RAG summarization loses key information.

Many teams implement RAG for summarization tasks, only to find their summaries missing critical information or containing irrelevant details. Figure 2 illustrates how information from the original content gets lost as it moves through a RAG pipeline. In this example, we are given a document which describes Blueteam AI's capabilities and how it works. Even though the user is asking for a summary of the document, only the first chunk appears relevant to the query and is included by the retriever. As a result, the resulting prompt misses important information about Blueteam AI such as how it works and the fact that it is differentiated by its ability to continuously protect data security.

RAG's capabilities do not align with summarization's requirements

Figure 3: The mismatch between RAG capabilities and summarization requirements

To understand why RAG falls short for summarization, let's examine its core capabilities versus what summarization actually needs. As shown in Figure 3, RAG excels at knowledge retrieval, external fact augmentation, query answering, and knowledge base integration. Meanwhile, summarization requires content distillation, key point extraction, context preservation, and length reduction.

While both share basic capabilities like text understanding and output generation, RAG's strengths are fundamentally misaligned with summarization's needs. It's like using a search engine when what you really need is a highlighter - you're adding complexity while missing the core task.

This misalignment explains why RAG-based summaries often include irrelevant external information while missing critical points from the original content. When you need to summarize a document or conversation, you don't need to find a needle in a haystack - you need to effectively distill what's already there.

Better Solutions

The key to effective summarization is choosing the right approach based on your content length and needs. Two commonly used approaches include stuffing and map reduce.

Stuffing: The Direct Approach

Figure 4: "Stuffing" summarization by feeding the entire content to the model prompt

For content within context window limits (like Gemini 1.5 Pro's 2M tokens or Claude's 100K tokens), you can simply "stuff" the entire text into the model. This is the simplest and often most effective approach when your content fits. The model can see all information at once, maintaining global context and coherence.

Map-Reduce: The Scalable Solution

Figure 5: "Map reduce" summarization by splitting content into chunks and summarizing iteratively

For longer content, we can borrow from functional programming's reduce pattern. Just as reduce uses an accumulator to build a final result, map-reduce summarization:

1. Maps: Splits content into chunks and summarizes each independently
2. Reduces: Combines these summaries iteratively, using each step's output as the accumulator for the next round

def map_reduce_summarize(text):
    # Map: Split and summarize chunks
    chunks = split_into_chunks(text)
    chunk_summaries = map(summarize, chunks)
    
    # Reduce: Combine summaries iteratively
    final_summary = reduce(
        lambda acc, chunk: summarize(acc + chunk),
        chunk_summaries
    )

This elegant approach scales to arbitrary length while maintaining coherence through the reduction process. However, it may incur higher latency as multiple dependent calls are made to a LLM.

Recommendations

Based on the comparison in Table 1, here's how to choose your summarization approach:

Choose Stuffing when:

• Your content fits within model context
• Quality is your top priority
• You need a quick, single-pass solution
• Cost and maintainability are concerns

Choose Map-Reduce when:

• Dealing with very long content
• Need fault tolerance and retry capability
• Can trade some quality for scalability
• Have parallel processing capabilities

The key is to start simple: first check if your content fits within context limits. If it does, stuffing is almost always the better choice. Only move to map-reduce when you actually need its scalability benefits.

A practical tip: many teams over-engineer their summarization pipeline with map-reduce when their content would fit comfortably in a single context window. Modern large-context models have made stuffing viable for most common use cases, including entire conversations, articles, and even short books.