AeroQuery

Ask questions about Canadian aviation regulations. Powered by RAG over the TC AIM.

Runs the same question with and without RAG so you can compare the answers side by side.

Eval Benchmark (RAGAS v0.4)

50 questions · 6 metrics · 3 models · Judged by GPT-5.4-mini via Azure AI Foundry

MetricGPT-5.4-miniGPT-3.5-turboLlama 3.2 1B (WebLLM)
BareRAG+RerankBareRAG+RerankBareRAG+Rerank
Factual Correctness0.320.400.43↑34%0.230.44↑91%0.44↑91%0.070.180.22↑214%
RAG triples Llama accuracy. Reranking adds another boost.
Faithfulness—0.820.88↑7%—0.820.85↑4%—0.520.55↑6%
GPT models stay grounded. Llama often ignores context.
Context Precision—0.780.92↑18%—0.780.92↑18%—0.780.93↑19%
Cohere rerank: 0.78 → 0.92. Right chunks, right order.
Context Recall—0.880.92↑5%—0.930.93—0.94↑4%0.90
94% recall — retrieval finds the right information.
Answer Relevancy0.84↑8%0.78↓7%0.79↓6%0.830.860.87↑5%0.79↑36%0.58↓27%0.62↓22%
Bare LLM scores higher by confidently hallucinating relevant-sounding answers. RAG correctly refuses when unsure — for aviation safety, faithfulness > relevancy.
Semantic Similarity0.690.77↑12%0.77↑12%0.740.80↑8%0.790.660.670.69↑5%
RAG answers are consistently closer to ground truth.

↑ Green = best score (improvement). ↓ Red = worst score (regression). Retrieval metrics are model-independent.

Built With

AI & Retrieval:OpenAIEmbeddings & LLM·PineconeVector DB·Cohere RerankReranking·WebLLMIn-browser LLM·OllamaLocal inference
Agentic:LangGraphAgent state machine·LangChainLLM orchestration
Eval & Ops:RAGAS6 metrics·LiteLLMMulti-provider·Azure AI FoundryJudge model
Infra:Azure FunctionsAPI·Next.jsFrontend·TailwindCSS·NetlifyHosting·DockerPipeline·PyMuPDFPDF parsing