Build intelligent applications with AI machine learning guides covering RAG architecture, LLM fine-tuning, prompt engineering techniques, and MLOps pipelines. Furthermore, discover how to deploy small language models on edge devices and build multimodal AI applications. Additionally, learn about AI agents, tool use patterns, and production-grade AI system design. Therefore, stay ahead in the rapidly evolving AI landscape with practical tutorials. Visit our about page for more information.
The model is not hallucinating — it is answering faithfully from bad context. Diagnose retrieval failures in order: is the passage in the index at all, does it rank, and is the embedding model actually the problem?
Why Is My RAG Pipeline Returning Irrelevant Chunks? Read Post »
vLLM serves open-weight LLMs at high throughput using PagedAttention and continuous batching. Learn GPU memory tuning, tensor parallelism, prefix caching, the latency-throughput trade-off, and when self-hosting actually beats an API.
Self-Hosted LLM Inference Serving with vLLM in Production Read Post »
A technical guide to building, securing, and deploying Model Context Protocol servers that connect large language models to your enterprise tools and data.
Model Context Protocol (MCP) Servers for Enterprise AI Tooling Read Post »
A production setup guide for LLM observability covering tracing, evaluations, cost attribution, and the trade-offs between Langfuse and Helicone.
LLM Observability with Langfuse and Helicone: Production Setup Guide Read Post »
Production patterns for Claude 4.7 with 1M token context: prompt caching, cost math, document analysis pipelines, and when to choose long context over RAG.
Claude 4.7 with 1M Context Window: Production Patterns Guide Read Post »
Production guide to fine-tuning LLMs with LoRA and QLoRA. Covers dataset curation, training configuration, evaluation metrics, and efficient deployment strategies.
Fine-Tuning LLMs with LoRA and QLoRA: Production Training Guide Read Post »
Deep dive into advanced RAG chunking strategies that improve retrieval accuracy. Covers semantic chunking, agentic RAG, graph-based retrieval, and hybrid approaches.
Advanced RAG Chunking Strategies: Semantic, Agentic, and Graph-Based Approaches Read Post »
Complete guide to Claude API tool use — function calling, structured outputs, multi-turn conversations, error handling, and production patterns.
Claude API Tool Use and Structured Outputs: Building Reliable AI Applications Read Post »
In-depth comparison of embedding models from OpenAI, Cohere, and BGE with benchmarks on retrieval quality, latency, cost, and deployment strategies for production search.
Embedding Models Compared: OpenAI, Cohere, and BGE for Semantic Search Read Post »