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.
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.
Stack
RAG + evaluation
Retrieval, chunking, and feedback loops that keep summaries grounded.
Problem
Summaries were fast, but not always trustworthy.
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
Ingest
Embed
Retrieve
Summarize
Verify
UX layer
- Structured summaries with sections
- Source highlights for transparency
- Fast iteration for prompt tuning
Architecture snapshot
System design for grounded outputs.
Runtime path
PDF ingest
Chunk + index
Top-k retrieve
Compose answer
request.trace
query -> retrieve contexts -> summarize -> attach citations
Reliability controls
- Confidence-aware fallback when retrieval is weak.
- Prompt regression checks on representative papers.
- Logging for latency, coverage, and citation density.
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.
Contact
Want to collaborate or adapt this system?