Do embedding models need to match for ingestion and user queries?

Yes. Embeddings only map to the same vector space if the models match. Mixing models breaks Cosine Similarity completely.

Why do I need a Re-ranker if I have a similarity search?

Similarity search fetches vectors mathematically close to the prompt. A Re-ranker evaluates those retrieved chunks for human-level contextual relevance before LLM generation.

How often should the Vector DB be updated?

Based entirely on how frequently your source data changes. Static contracts can be weekly; dynamic tickets require event-driven updates.

Can RAG guarantee the AI won't lie?

It vastly reduces 'hallucinations', but since the AI generates the final words, there's always a tiny chance. That's why RAG provides source citations!

Guide

Retrieval-Augmented Generation

From preventing AI hallucinations to advanced chunking strategies and quantitative metrics, discover the definitive architecture powering modern AI.

Select Your Persona:

The Power of RAG: Grounding & Prevention

Architecture Flow Diagram

Document Chunking Strategies

Architectures & Use Cases

Limitations & Constraints

Evaluation & Metrics

Common Misconceptions

RAG vs. Fine-Tuning vs. RAFT

Frequently Asked Questions

Test Your Knowledge

Answer the 3 questions below tailored to your selected persona to check your understanding.