- 0% hallucination rate
- 83% accuracy across 61 tasks
- 4-layer verification system
Most RAG APIs generate answers.
We verify them.
After testing 14 LLMs across 61 evaluation tasks, our pipeline maintains 0% hallucination rate at 83% accuracy — in production conditions.
Here’s exactly how we did it.
The Problem Nobody Talks About
RAG is supposed to reduce hallucinations.
In reality, most implementations just move the problem.
They retrieve documents…
then blindly trust the model to interpret them correctly.
The result?
- Missing critical facts
- Conflicting sources ignored
- Confident but wrong answers
And worst of all: no verification layer.
Most RAG systems don’t actually know if their answer is grounded.
They just hope it is.
Our Approach: A 4-Layer Defense System
We designed our pipeline with one goal:
Make hallucination structurally impossible — not just unlikely.
Layer 1: Retrieval That Doesn’t Miss
We use a hybrid retrieval system:
- BM25 → precise keyword matching
- Vector search → semantic recall
But the key isn’t hybrid search.
It’s how we handle failure cases.
- If retrieval is weak → downstream layers compensate
- If retrieval is strong → we stay fast
👉 Retrieval is treated as a signal, not a source of truth.
Layer 2: Slot-Based Critical Chunks
Most RAG pipelines rank chunks and pick the top K.
We don’t.
We introduced a slot-based system:
- Detect critical query intents (numbers, entities, dates)
- Force-include matching chunks in the context
This ensures:
- No critical data is dropped
- No reliance on ranking luck
👉 It’s constraint-based, not score-based.
Layer 3: Deterministic Key Facts Injection
Before calling the LLM, we extract key facts directly from the context:
- Numbers
- Dates
- Percentages
- Identifiers
Then inject them into the prompt as non-negotiable facts.
This removes ambiguity entirely.
The model doesn’t “guess” values anymore.
It anchors to verified data.
Layer 4: Post-Generation Grounding Check
This is where most systems stop.
We don’t.
After generation, we run a grounding verification step:
- Extract terms from the answer
- Check if ≥60% exist in the retrieved context
- If not → reject or flag
This creates a closed-loop system:
No grounded context → no valid answer.
Benchmarks (Real Numbers)
We evaluated the system across 61 tasks and 14 LLMs.
| Metric | Score |
|---|---|
| Eval (61 tasks) | 83% |
| Hallucination rate | 0% |
| RAG retrieval | 88% |
| Cross-doc comparison | 93% |
| Avg latency | 1.2s |
Key insight:
You don’t need 100% accuracy to achieve 0% hallucination.
You need verification.
What Didn’t Work (And Why It Matters)
We tried multiple “obvious” improvements that failed:
- Multi-step retrieval → added noise, reduced precision
- Header penalties → broke valid top chunks
- Over-aggressive reranking → increased variance
Lesson:
RAG is a balanced system, not a collection of optimizations.
Small changes can silently degrade performance.
Why This Approach Works
Most systems try to make the model smarter.
We did the opposite:
- Reduce model freedom
- Increase constraints
- Add verification
👉 The result is not just better answers.
👉 It’s reliable answers.
Try It
We made the API available publicly:
- Free tier on Rapidapi
- Docs: https://wauldo.com/docs
If you're building with RAG, this will save you months of trial and error.
Final Thought
Hallucination isn’t a model problem.
It’s a system design problem.
Solve it at the architecture level —
and the model becomes predictable.
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