Eliminating the Precision–Latency Trade-Off in Large-Scale RAG
Retrieval-Augmented Generation (RAG) systems constantly face a trade-off: Precise results often mean higher latency and cost, while faster responses risk losing context and accuracy. The solution isn’t choosing one or the other. It’s redesigning retrieval. Let’s explore three techniques that together eliminate this trade-off: multiphase ranking, layered retrieval and semantic chunking.
When combined, they create a retrieval stack that balances speed, scalability and precision.
Multiphase Ranking: Incremental Refinement of Results
At the heart of retrieval lies ranking. Running deep machine learning (ML) ranking across the entire candidate set introduces increased latency and increases infrastructure cost. On the other hand, lightweight scoring methods alone can’t capture enough context, so precision suffers. That’s why a multiphased approach provides a balanced alternative.
Instead of choosing between expensive deep models or fast but shallow heuristics, multiphase ranking stages scoring from cheap to costly. Lightweight filters (lexical, approximate nearest neighbor or ANN) quickly trim the candidate pool, while progressively heavier ML functions are applied only to the top results. This preserves precision while keeping latency and compute under control.
Multiphase ranking provides a balanced alternative:
- Phase 1: Fast filtering using keyword matching or ANN search.
- Phase 2: Reranking with dense embeddings, hybrid similarity measures or custom ranking expressions.
- Phase 3+: Advanced machine-learned models, personalization signals or domain-specific scoring rules.
This staged refinement ensures that expensive models are applied only where they add the most value.
Benefits include:
- Cost-aware precision: Spend compute strategically across phases.
- Hybrid logic: Blend symbolic rules, semantic similarity and behavioral data.
- Personalization: Adapt results to individual users or sessions.
By mirroring these best practices in large-scale search and recommendation, multiphase ranking enables RAG systems to deliver accurate results without breaking latency budgets.
Layered Retrieval: The Foundation of Ranking Quality
Even the most sophisticated multiphase ranking stack can’t compensate for poor retrieval units or noisy inputs. The quality of ranking depends heavily on the retrieval unit you choose:
- Fine-grained chunks (paragraphs or sliding windows) maximize recall, since even short queries are likely to match. But they introduce trade-offs:
- Context fragmentation: Key signals get split across chunks.
- Redundancy: Overlapping chunks inflate index size and cause duplicates.
- Downstream burden: Ranking and large language models (LLMs) must stitch fragmented evidence together, increasing token usage and latency.
- Whole-document retrieval preserves global context and reduces redundancy, but often sacrifices precision. Large spans of irrelevant text are pulled into prompts, diluting relevance signals, inflating token costs and making reranking less effective.
A well-designed retrieval strategy typically lands in between: defining a semantic retrieval unit that captures enough local context to be self-contained, while still preserving structural metadata (headings, sections, timestamps) that downstream ranking can exploit. This balance ensures that ranking operates over high-quality candidates, minimizing wasted compute and maximizing the signal-to-noise ratio that feeds the LLM.
Layered retrieval achieves this balance by combining both levels of relevance:
- Rank and select the most relevant documents.
- Within those documents, retrieve only the top-K chunks.
This hierarchical process preserves the broader context of document-level signals while narrowing down to the specific spans that matter.
Benefits include:
- Reduced token usage and lower prompt costs.
- Cleaner, more coherent context for the LLM.
- Improved precision without sacrificing recall.
Semantic Chunking: Precision Starts With Preprocessing
Finally, retrieval quality depends on how you index your data. Long-form documents stored as monoliths often produce noisy retrieval, because only part of the content is relevant to a given query.
Semantic chunking addresses this by splitting documents into meaningful, self-contained units like paragraphs or logical sections while retaining contextual metadata like headings, authorship or timestamps.
Benefits include:
- Higher recall: More granular entry points into documents.
- Better precision: Irrelevant sections can be excluded at query time.
- Metadata enrichment: Supports symbolic filtering and downstream ranking.
Chunking can increase index size and requires careful prompt assembly, but when combined with layered retrieval and multiphase ranking, it becomes a powerful foundation for precision.
Building a Production-Ready Retrieval Stack for RAG
Together, these three techniques address the biggest pain points in scaling RAG:
- Overlong prompts from including too much content.
- Context fragmentation from isolated chunks.
- Rigid ranking pipelines that ignore domain logic.
A robust retrieval stack should therefore:
- Index documents with semantic chunking while preserving metadata.
- Retrieve hierarchically through layered retrieval.
- Refine results efficiently with multiphase ranking.
This combination enables more accurate, cost-efficient and trustworthy LLM outputs, especially when paired with retrieval-aware prompt engineering.
Final Thoughts
As RAG systems scale, retrieval design becomes a key differentiator. Moving beyond simple vector or ANN search to incorporate multiphase ranking, layered retrieval and semantic chunking dramatically improves both efficiency and output quality.
Vespa was built to handle these retrieval challenges at enterprise scale. Its tensor-native architecture supports multiphase ranking, layered retrieval and semantic chunking directly in-cluster, eliminating external bottlenecks and costly workarounds. By running retrieval and ranking where the data lives, Vespa delivers low-latency, high-precision results across billions of documents and thousands of queries per second.
Whether you’re building knowledge assistants, research agents or large-scale production RAG systems, Vespa provides the retrieval foundation that keeps generative AI accurate, efficient and ready to scale.
Learn how Vespa powers retrieval at Perplexity.
