RAG Architecture Patterns: When Vector Search Is Not Enough
Design Retrieval-Augmented Generation systems that actually work in production. Covers chunking strategies, embedding models, hybrid search, reranking, evaluation metrics, and the failure modes that textbook RAG implementations ignore.
LLM Application Architecture: Beyond the API Call
Design production LLM applications that are reliable, cost-efficient, and maintainable. Covers prompt engineering patterns, model routing, caching strategies, evaluation frameworks, and the operational patterns for running LLMs at scale.
AI Model Evaluation and Testing: Measuring What Matters
Build evaluation frameworks for ML and LLM applications that catch regressions before users do. Covers offline metrics, online metrics, regression test suites, human evaluation, bias detection, and the evaluation-driven development workflow.
Prompt Engineering for Developers: Getting Reliable Output from LLMs
Write prompts that produce consistent, high-quality output from large language models in production systems. Covers prompt structure, few-shot learning, chain-of-thought, output formatting, guardrails, evaluation, and the patterns that turn unpredictable AI into reliable software components.
Prompt Engineering Patterns for Production Systems
Battle-tested prompt engineering patterns for production AI systems. Covers chain-of-thought, few-shot templates, guardrails, output parsing, and systematic prompt versioning.
LLM Evaluation Frameworks for Enterprise Deployments
How to evaluate LLM performance in production. Covers automated benchmarking, human evaluation workflows, regression testing, and continuous monitoring for enterprise AI systems.
Vector Database Selection Guide for RAG Systems
How to choose the right vector database for retrieval-augmented generation. Compares Pinecone, Weaviate, Qdrant, Milvus, pgvector, and ChromaDB across performance, cost, and operational complexity.
Model Serving Infrastructure at Scale
How to build reliable model serving infrastructure for production AI. Covers inference optimization, GPU orchestration, batching strategies, model routing, and cost management.
Enterprise RAG Pipeline Design and Optimization
How to build production-grade RAG pipelines for enterprise knowledge systems. Covers chunking strategies, hybrid search, reranking, context management, and evaluation methodologies.
AI Agent Orchestration: Building Multi-Agent Systems That Actually Work
Architectural patterns for orchestrating AI agents — routing, chaining, delegation, tool use, memory systems, and reliability engineering for production agent deployments.