Engineering Reference Guides
Built for Production
Step-by-step tactical guides derived from real enterprise deployments. From cloud cost optimization to AI infrastructure — find the answer, ship the fix.
Engineering Categories
Cloud Architecture
AWS, Azure, GCP infrastructure patterns, cost optimization, and migration playbooks.
13 guides →Data Engineering
ETL pipelines, data lakes, streaming architectures, and analytics platforms.
24 guides →AI & Machine Learning
LLM deployment, model serving, MLOps pipelines, and AI infrastructure.
27 guides →Security & Compliance
Zero trust, identity management, secrets handling, and compliance frameworks.
19 guides →Software Engineering
CI/CD, containerization, microservices, API design, and DevOps practices.
24 guides →ERP & Enterprise
D365, Power Platform, enterprise integrations, and business process automation.
15 guides →Database Engineering
PostgreSQL, SQL Server, NoSQL, performance tuning, and migration strategies.
15 guides →Reference
Quick-reference tables, cheat sheets, and command lookups.
1 guide →Start Here
AI Ethics & Governance Framework
Build enterprise AI governance. Covers ethics principles, risk assessment, review boards, model cards, transparency reporting, regulatory compliance, and organizational maturity.
Read guide → ai-engineeringAI 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.
Read guide → ai-engineeringLLM 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.
Read guide → ai-engineeringPrompt 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.
Read guide → ai-engineeringRAG 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.
Read guide → AI & Machine LearningResponsible AI: Bias Detection & Mitigation
Detect and fix bias in AI/ML systems. Covers bias types, fairness metrics, testing frameworks, mitigation techniques, regulatory compliance, and building responsible AI governance.
Read guide →Part of Something Bigger
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