Eval-first RAG infrastructure
Build RAG systems you can actually measure.
Retrieve better context. Generate grounded answers. Know when your system should refuse — with evaluation, traces, and CI gates built in.
Query pipeline
Query
Rewrite
Hybrid retrieval
RRF
Rerank
Ground
Answer
Why ContextIQ
Evaluation snapshot
75 golden queries · structural · hybrid_rerank
Context Recall
89.3%
How often the relevant source information was successfully retrieved.
Context Precision
41.6%
Share of retrieved chunks that are actually relevant to the question.
Faithfulness
100%
Evaluated with extractive generation
Answer Relevancy
68.7%
How well the answer addresses the user's question.
Refusal Accuracy
100%
Correctly refusing when the corpus cannot support an answer.
End-to-end pipeline
- 01
Ingest
Fetch public docs into a versioned raw zone.
- 02
Parse
Preserve headings, tables, lists, and code structure.
- 03
Chunk
Structural chunking tuned for technical documentation.
- 04
Embed
Dense vectors (BGE-small) with optional hash fallback.
- 05
Understand query
Route, rewrite, and optional HyDE expansion.
- 06
Hybrid retrieve
Dense cosine + BM25 sparse search in parallel.
- 07
Generate
Extractive, Ollama, or Bedrock grounded generation.
- 08
Ground / refuse
Cite sources or refuse when evidence is thin.
Stack
See how an answer was produced.
Open RAG playground