Everything we know about building production AI systems — organized by topic. Technical articles, architecture guides, and service deep-dives from the Crazy Unicorns engineering team.
Build production-grade Retrieval-Augmented Generation pipelines. From chunking strategies and vector databases to hybrid search and evaluation frameworks.
Hard-won lessons about chunking, evaluation, hybrid search, and monitoring from real enterprise deployments.
When to use retrieval-augmented generation vs model fine-tuning, with a practical decision matrix.
Hands-on comparison of Pinecone, Weaviate, Qdrant, Milvus, and pgvector in production workloads.
End-to-end RAG pipeline design, implementation, and optimization for enterprise knowledge bases.
Custom LLM solutions including RAG, fine-tuning, and prompt engineering for production use.
Design, build, and deploy AI agents that plan, reason, and take actions. Covers guardrails, multi-agent orchestration, and tool-use patterns.
How to architect agent systems that are safe, observable, and controllable with layered guardrails.
Patterns for orchestrating multiple specialized agents — from supervisor hierarchies to swarm architectures.
Custom AI agent design with tool integration, planning capabilities, and human-in-the-loop controls.
Automate complex workflows with intelligent agents that handle exceptions and edge cases.
Practical techniques for building, evaluating, and optimizing Large Language Model applications. Covers testing frameworks, fine-tuning, and prompt engineering.
A systematic framework covering golden datasets, LLM-as-judge, and continuous monitoring for quality.
When fine-tuning makes sense over RAG, with cost analysis and implementation considerations.
Domain-specific model fine-tuning with data preparation, training, and evaluation pipelines.
Full-stack LLM application development from prototype to production deployment.
Design scalable, secure AI infrastructure for enterprise environments. Covers gateway patterns, model routing, MLOps, and platform engineering.
Gateway design, model routing, security patterns, and cost management for enterprise-scale AI.
Practical strategies for keeping LLM costs predictable — caching, model routing, and prompt optimization.
Design and implement production AI infrastructure with observability, scaling, and governance.
End-to-end enterprise AI strategy, implementation, and change management.
Connect AI systems with existing enterprise infrastructure, APIs, and data pipelines.
Protect LLM applications against prompt injection, data leakage, and adversarial attacks. Covers OWASP LLM Top 10, compliance frameworks, and security testing.
Defense strategies for prompt injection, data exfiltration, and adversarial attacks on LLM systems.
Safety layers for autonomous AI systems — input validation, action boundaries, and output verification.
Security-first AI strategy including risk assessment, compliance planning, and governance frameworks.
Secure infrastructure design with access controls, audit logging, and threat monitoring.
Measure and maximize the business impact of AI investments. Covers ROI frameworks, cost optimization, build-vs-buy decisions, and organizational readiness.
A practical framework for quantifying AI automation ROI with concrete metrics and benchmarks.
Keep LLM costs predictable with caching, model routing, prompt optimization, and tiered architectures.
Strategic AI advisory — opportunity assessment, roadmap planning, and vendor evaluation.
Full enterprise AI transformation from strategy through implementation and scaling.
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