7 articles
Build a production semantic search engine using OpenAI embeddings and cosine similarity in Python. Covers hybrid search, caching, FastAPI deployment, and cost optimization for high-volume use cases.
Master HuggingFace Transformers in 2026: sentiment analysis, NER, summarization, embeddings, image classification, and Whisper speech recognition. Production deployment with FastAPI included.
Understand how text embeddings enable semantic search — from word vectors to transformer-based embeddings, cosine similarity, and practical implementation for RAG systems. The foundation of every LLM retrieval application.
Generate high-quality text embeddings locally using Sentence Transformers — no API keys, no cost per call. Covers model selection, batch encoding, fine-tuning, and integration with vector databases for production RAG.
Compare zero-shot, few-shot, embedding-based, and fine-tuned classification approaches with production trade-offs.
Build production DPR systems: train dual encoders, fine-tune on domain data, scale with FAISS, and outperform BM25 on specialized domains.
Transform user queries to improve retrieval with rewriting, HyDE, step-back prompting, and multi-hop decomposition techniques that boost RAG accuracy.