Query our demo knowledge base powered by production-grade RAG
Suggested queries:
AI chatbot trained on RAG methodology. Can answer: "Why is my payback so fast?" "What if I have 100 employees instead?" Escalates complex questions to human.
Real-time performance statistics from our demo system
See what RAG can do with these real-world examples
See exactly how our RAG system processes your queries
# This is what powers the demo above
async def answer_query(query: str, dataset: str):
# Step 1: Embed query
query_vector = embedding_model.encode(query)
# Step 2: Retrieve similar documents
results = vector_db.search(
vector=query_vector,
dataset=dataset,
top_k=5
)
# Step 3: Rerank for relevance
reranked = reranker.score(query, results)
top_docs = reranked[:3]
# Step 4: Generate answer
context = format_context(top_docs)
prompt = f"""Context:
{context}
Question: {query}
Answer the question using only the context provided.
Cite sources using [1], [2] notation."""
answer = await llm.generate(prompt)
return {
"answer": answer,
"sources": top_docs,
"confidence": calculate_confidence(reranked),
"latency": measure_latency()
}
Honesty builds trust - here's what this demo doesn't show
Demo uses fixed datasets that don't update in real-time
5,000 documents vs enterprise 1M+ docs
No proprietary or confidential information
Production systems have more components
Automatic updates as new documents arrive
Handle enterprise-scale document repositories
Document-level permissions and audit trails
Fine-tuned embeddings for your domain
RAG vs other approaches for enterprise search
| Metric | This Demo (RAG) | Base LLM Only | Keyword Search |
|---|---|---|---|
| Answer Quality | Detailed, accurate with citations ✅ | Generic, no sources ⚠️ | List of documents ❌ |
| Response Time | 1.2 seconds | 0.5 seconds | 0.2 seconds |
| Accuracy | 95% (with sources) | 70% (unverifiable) | 30% (user work required) |
| Cost per Query | $0.02 | $0.01 | $0.001 |
| Best For | Enterprise knowledge retrieval | General knowledge questions | Simple keyword matching |
Takeaway: RAG balances accuracy, speed, and usability for enterprise knowledge
Learn More About RAGLet's discuss how RAG can transform your enterprise knowledge management