Day 36 :How to Build a High-Accuracy RAG Pipeline: Bi-Encoders,Cross-Encoders, n Metadata Filtering

Описание: Are you building AI search systems that are "semantically correct" but practically useless? Welcome to *Day 36 of my 150-day journey learning AI!* 🧠

Today, we’re moving beyond basic embeddings. While semantic search is powerful, it often fails in the real world—like showing a £1,200 laptop when the user asked for one under £1,000. In this video, we dive deep into the "guardrails" of modern AI: **Payload Filtering**, **Hybrid Search**, and **Semantic Caching**.

*In this video, you will learn:*

*The Power of Filtering:* Why semantic search alone isn't enough and how to use metadata (payloads) to enforce real-world constraints like price, category, and date,.
*Pre-Filtering vs. Post-Filtering:* Understanding the trade-offs between narrowing your dataset before you search versus refining results after the fact,.
*The "Hybrid Funnel":* How to combine *Bi-Encoders* for massive speed with *Cross-Encoders* as an "accuracy judge" to ensure the highest quality results,,.
*Saving 40-60% on API Costs:* A deep dive into **Semantic Caching**—matching queries by meaning to avoid expensive and slow LLM calls,.
*Hybrid Search & Reranking:* Why combining keyword search (BM25) with vector search is the only way to catch abbreviations like "GAN" or specific names that get "lost" in embeddings,,.
*Content Design for RAG:* Pro-tips on how simplifying your source documentation (like using summaries and simple tables) can drastically improve your AI’s performance,.

*Key Takeaways:*
Building production-ready RAG (Retrieval-Augmented Generation) is about more than just vectors; it’s about the interplay between structured and unstructured data,. Whether you are using Qdrant, FAISS, or ChromaDB, these strategies are essential for scaling from a prototype to a real enterprise solution,.

*Timestamps:*
0:00 - The problem with "pure" Semantic Search
2:15 - Payload Filtering explained
4:40 - Pre-filtering vs. Post-filtering mechanics
7:10 - How Semantic Caching saves you money
10:05 - Hybrid Search: Keywords + Vectors
13:30 - The Reranking Funnel (Bi-Encoders vs. Cross-Encoders)
16:00 - Content Strategy: Making your data RAG-ready
18:45 - Summary & Day 36 Reflections

*Resources Mentioned:*
Qdrant Filtering Guide
Semantic Caching Implementation (OneUptime)
Hybrid Search Architecture (Superlinked)
Enterprise RAG Optimization (IBM Research/arXiv)


#AI #VectorSearch #RAG #MachineLearning #SemanticSearch #HybridSearch #LLMOps #Qdrant #150DaysOfAI #TechTutorial