01
Invented statistics
The model producing plausible but false numbers about your documents.
AI answers with false information and no citations
Reducing AI hallucinations: RAG accuracy and citation checks
Make AI answers traceable: better parsing and retrieval, rerankers, and automated checks that each claim in an answer is supported by a cited source.
Symptoms
Signs your platform has this problem
If several of these sound familiar, the plan below is where we would start.
02
No source citations
Users unable to check which page or passage an answer came from.
03
Noisy context
Naive chunking filling the prompt with irrelevant text that confuses the model.
Remediation plan
How we fix it, step by step
Each phase ends with a measurement, so you can see what changed before the next one starts.
01
Layout-aware parsing
Replacing naive text chunkers with parsers that keep headings and table structure.
02
Hybrid search
Combining vector embeddings with BM25 keyword search through reciprocal rank fusion.
03
Cross-encoder reranking
Reranking retrieved chunks (for example the top 50 down to the best 5) before they reach the model.
04
Automated citation checks
An evaluation step (for example with DeepEval) that checks each claim in an answer against the passage it cites.
Technical checklist
Remediation checklist
What we check before a change goes to production:
- Use layout-aware parsing for multi-page tables and PDFs
- Deploy hybrid search combining dense vectors with BM25 keywords
- Add cross-encoder reranking to drop irrelevant context chunks
- Enforce structured outputs with citations, and check them automatically
What we measure
We take a baseline first and report the same measurements after each change, from your own monitoring — evidence, not promised results.
- Groundedness
- Share of answer claims supported by a cited passage, on an evaluation set
- Retrieval recall
- Relevant passages found in the top results, per question
- Latency
- End-to-end answer time at p95
Related service
AI development
LLM systems built for compliance review: schema-validated extraction, human-in-the-loop workflows, and audit trails — measured in cycle time, not demos.
Explore AI developmentQuestions
Questions about this remediation
Standard chunkers slice tables into meaningless fragments. Layout-aware parsers keep table headers and cell relationships together, for example as Markdown tables.
An evaluation step extracts the claims from each answer and checks every one against the retrieved passages, flagging unsupported claims for review. It runs on a fixed evaluation set before every release.
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Want an engineer to look at this with you?
Send us the symptoms and any metrics you have. We'll reply within one business day, set up a call and agree what to measure before anything changes.