Building an LLM-based Chatbot is easy, but making it 'smart' and accurate with your company data is the real challenge. The key lies in RAG (Retrieval-Augmented Generation) and the use of Vector Databases. Many beginner developers just embed raw data and hope for a miracle. The result? Irrelevant searches and AI that keeps hallucinating. This tutorial dissects optimization techniques for Chunking and Hybrid Search.
Critical Chunking Strategy
A common mistake is cutting text (chunking) based on fixed character counts (e.g., 500 chars). This cuts sentence context. The scientific solution is Semantic Chunking. Use NLP libraries to cut text based on meaning similarity, not just spaces. Ensure there is a 10-15% overlap between chunks so that context on the borders is not lost during retrieval.
Hybrid Search: Keyword + Vector
Don't just rely on Vector Search (semantic meaning search). Sometimes users search for specific keywords (e.g., product code SKU-123) that are hard for pure vector search to catch. Implement Hybrid Search combining BM25 algorithm (traditional keyword matching) with Cosine Similarity (vector matching). Apply dynamic weighting using Reciprocal Rank Fusion (RRF) techniques to merge results from both methods.
Finally, use Metadata Filtering. Before performing heavy vector searches, filter data first based on categories (e.g., Year, Department, Document Type). This speeds up query time by up to 50x and drastically improves accuracy. At CybermaXia, this technique is a mandatory standard in every Enterprise AI solution deployment.