Case study

RAG Summarizer

A retrieval-augmented system that condenses long research documents into readable, structured summaries with grounded citations. The goal was to make dense technical content easier to understand without sacrificing accuracy or provenance.

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Stack

RAG + evaluation

Retrieval, chunking, and feedback loops that keep summaries grounded.

Problem

Summaries were fast, but not always trustworthy.

Why it mattered

Researchers and engineers needed quick comprehension of long papers, but generic LLM outputs often missed key context or hallucinated details. The system had to balance speed, accuracy, and traceability.

Goals

  • Keep answers grounded in source material
  • Preserve technical detail without overwhelming users
  • Deliver summaries quickly and consistently

Constraints

Trust, latency, and clear provenance.

System constraints

  • Large PDFs with mixed layouts and charts
  • Latency budgets for interactive use
  • Need for transparent citations

Quality constraints

  • Consistent retrieval coverage
  • Reduced hallucination risk
  • Readable structure for non-experts

Solution

RAG pipeline with evaluation hooks.

Pipeline overview

  • Document ingestion and chunking strategy
  • Retriever with relevance scoring
  • LLM summarization with citation stitching

UX layer

  • Structured summaries with sections
  • Source highlights for transparency
  • Fast iteration for prompt tuning

Validation

Measured groundedness and clarity.

Evaluation signals

  • Retrieval coverage and citation consistency
  • Human review of summary clarity
  • Failure-case analysis for hallucinations

Operational checks

  • Latency tracking by document size
  • Prompt regression testing
  • Logging for repeatability

Results

Reliable summaries with clear provenance.

Outcomes

  • Grounded summaries with traceable sources
  • Consistent structure for fast scanning
  • Evaluation framework ready for expansion

Next steps

  • Add reranking and hybrid retrieval
  • Expand domain-specific evaluation sets
  • Integrate user feedback loops

Links

Explore the project.

Repository

Open-source code and documentation.

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