AI Engineering
RAG systems, LLM guardrails, prompt engineering, model compression, and production AI patterns.
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.
LLM Application Testing
Test LLM-powered applications for correctness, safety, and reliability. Covers evaluation frameworks, regression testing for prompts, adversarial testing, benchmark design, cost testing, and the patterns that make LLM applications production-ready.
RAG Architecture Patterns
Design production-ready Retrieval-Augmented Generation systems. Covers chunking strategies, embedding models, vector search, reranking, context window optimization, hybrid search, evaluation frameworks, and the patterns that make RAG reliable.
LLM Fine-Tuning
Fine-tune large language models for domain-specific tasks. Covers full fine-tuning, LoRA, QLoRA, dataset preparation, evaluation, deployment, and the patterns that produce specialized models without the cost of training from scratch.
LLM Guardrails and Safety
Implement safety guardrails for LLM-powered applications. Covers input validation, output filtering, content policies, jailbreak prevention, PII redaction, and the patterns that make LLM applications safe for production use.
AI Agent Architecture
Design and build autonomous AI agents that can plan, reason, and take action. Covers agent loops, tool use, memory systems, multi-agent orchestration, guardrails, and the patterns that make AI agents reliable and controllable.
Vector Database Engineering
Build and operate vector databases for similarity search at scale. Covers embedding storage, approximate nearest neighbor algorithms, index types, hybrid search, vector database selection, and the patterns that make semantic search fast and accurate.
Explainable AI Engineering
Make machine learning model decisions interpretable and transparent. Covers SHAP values, LIME explanations, feature importance, model cards, interpretability vs. accuracy tradeoffs, and the patterns that build trust in ML predictions.
LLM Evaluation and Benchmarking
Systematically evaluate large language model performance for your use case. Covers evaluation frameworks, hallucination detection, human evaluation, automated metrics, A/B testing LLMs, and the patterns that prevent shipping LLM features that look impressive in demos but fail in production.
AI Agent Orchestration
Design and build AI agent systems that coordinate multiple LLM calls to solve complex tasks. Covers agent architectures, tool use, planning loops, memory management, error recovery, and the patterns that make multi-step AI workflows reliable.
Prompt Engineering Patterns
Design effective prompts for large language models in production systems. Covers chain-of-thought prompting, few-shot learning, system prompt design, structured output, prompt testing, and the patterns that make LLM interactions reliable and repeatable.
AI Training Data Engineering
Build and manage training datasets for machine learning systems. Covers data collection strategies, labeling pipelines, data quality frameworks, active learning, synthetic data generation, and the patterns that determine whether your ML model learns the right lessons.
Model Compression Techniques
Deploy machine learning models efficiently on edge devices and in production. Covers quantization, pruning, knowledge distillation, and the patterns that reduce model size by 10x while retaining 95% accuracy.
AI Model Monitoring and Drift Detection
Monitor deployed ML models for performance degradation and data drift. Covers feature drift detection, prediction monitoring, model staleness indicators, automated retraining triggers, and the patterns that ensure AI systems stay accurate after deployment.
AI Agent Tool Selection Optimization
Production-ready guide covering ai agent tool selection optimization with implementation patterns, code examples, and anti-patterns for enterprise engineering teams.
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.
AI Agent Memory Architecture Design Patterns
Production-ready guide covering ai agent memory architecture design patterns with implementation patterns, code examples, and anti-patterns for enterprise engineering teams.
AI Cost Optimization
Control LLM spending with token budgeting, model tiering, prompt compression, and batch inference strategies.
AI Feature Flags
Roll out AI features safely with model-version flags, A/B testing, gradual rollouts, and automatic rollback on quality regression.
AI Gateway Architecture
Build a centralized AI gateway for routing, rate limiting, cost tracking, and model fallback across multiple LLM providers.
AI Observability
Monitor LLM applications in production with trace logging, token usage dashboards, latency percentiles, and quality score tracking.
AI Safety and Alignment
Implement constitutional AI principles, RLHF guardrails, and output filtering to keep production AI systems safe and aligned.
Context Window Management
Maximize LLM context utilization with sliding windows, summarization chains, and priority-based message pruning strategies.
Embedding Model Selection
Choose the right embedding model for your use case by comparing dimensionality, latency, multilingual support, and MTEB benchmarks.
Fine-Tuning Pipelines
Build reproducible fine-tuning workflows with data preparation, hyperparameter selection, evaluation, and deployment automation.
Function Calling Patterns
Design reliable LLM function-calling interfaces with schema validation, retry logic, and parallel tool execution.
LLM Output Parsing
Parse structured data from LLM responses reliably using JSON mode, XML tags, Pydantic validators, and retry-with-feedback loops.
Multi-Modal AI Pipelines
Orchestrate text, image, audio, and video models into unified pipelines for complex document processing and content generation.
Prompt Caching Strategies
Reduce LLM inference costs by 80% with prefix caching, KV-cache reuse, and semantic deduplication techniques.
RAG Evaluation Metrics
Measure retrieval-augmented generation quality with faithfulness, relevance, and answer correctness scoring frameworks.
Advanced Retrieval Strategies
Go beyond naive RAG with parent-document retrieval, hypothetical document embeddings, and multi-index fusion techniques.
Semantic Search Architecture
Design hybrid search systems combining vector similarity, BM25 keyword matching, and cross-encoder reranking for production accuracy.
Streaming LLM Responses
Implement server-sent events and WebSocket streaming for real-time LLM response delivery with proper backpressure handling.
Vector Database Operations
Manage vector indexes at scale with partitioning, metadata filtering, index maintenance, and disaster recovery procedures.
Vision API Pipelines
Build production image understanding pipelines with GPT-4V, Claude Vision, and open-source VLMs for document extraction and analysis.
Ai Anomaly Detection Systems
Production-grade guide to ai anomaly detection systems covering architecture patterns, implementation strategies, testing approaches, and operational best practices for enterprise engineering teams.
Ai Capacity Prediction
Production-grade guide to ai capacity prediction covering architecture patterns, implementation strategies, testing approaches, and operational best practices for enterprise engineering teams.
Ai Code Review Automation
Production-grade guide to ai code review automation covering architecture patterns, implementation strategies, testing approaches, and operational best practices for enterprise engineering teams.
Ai Documentation Generation
Production-grade guide to ai documentation generation covering architecture patterns, implementation strategies, testing approaches, and operational best practices for enterprise engineering teams.
Ai Experiment Tracking
Production-grade guide to ai experiment tracking covering architecture patterns, implementation strategies, testing approaches, and operational best practices for enterprise engineering teams.
Ai Feature Extraction Pipelines
Production-grade guide to ai feature extraction pipelines covering architecture patterns, implementation strategies, testing approaches, and operational best practices for enterprise engineering teams.
Ai Incident Diagnosis
Production-grade guide to ai incident diagnosis covering architecture patterns, implementation strategies, testing approaches, and operational best practices for enterprise engineering teams.
Ai Model Governance
Production-grade guide to ai model governance covering architecture patterns, implementation strategies, testing approaches, and operational best practices for enterprise engineering teams.
Ai Pair Programming Patterns
Production-grade guide to ai pair programming patterns covering architecture patterns, implementation strategies, testing approaches, and operational best practices for enterprise engineering teams.
Ai Test Generation
Production-grade guide to ai test generation covering architecture patterns, implementation strategies, testing approaches, and operational best practices for enterprise engineering teams.