Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Set up a secure API gateway for AI agent systems using Kong, Traefik, and custom FastAPI patterns. Covers authentication plugins, rate limiting, request transformation, and routing strategies.
Discover how self-consistency prompting improves LLM accuracy by sampling multiple reasoning paths and using majority voting to select the most reliable answer.
Learn how Self-RAG enables language models to decide when to retrieve, evaluate their own outputs for relevance and support, and regenerate when quality is insufficient. Full implementation guide.
Explore how self-reflection transforms AI agents from one-shot executors into iterative improvers — covering critique loops, retry-with-feedback, score-and-improve patterns, and practical Python implementations.
Learn how Semantic Kernel brings enterprise-grade agent capabilities to .NET and Python applications with planners, plugins, memory integration, and deep Azure ecosystem support.
Learn how to build a semantic memory system for AI agents using text embeddings, similarity thresholds, and memory consolidation to retrieve the most relevant facts from past interactions.
Build an intelligent autocomplete system that suggests semantically relevant queries as users type, combining query embeddings with popularity weighting and user personalization for a superior search experience.
Elasticsearch hybrid search bm25 dense vector knn documentation: Configure Elasticsearch for hybrid search that combines traditional BM25 keyword matching with dense vector kNN search, giving you the precision of neural retrieval with the reliabil…
Master the essential metrics for evaluating semantic search quality — nDCG, MRR, and Recall@K — with practical Python implementations, test set creation methodology, and benchmarking workflows.
