AI engineering for production systems: advanced RAG with re-ranking, chunking strategies, vector stores, guardrails, evaluation frameworks, and multi-agent architectures.
This series collects 27 articles (approximately 368 minutes of total reading time), designed for a level Advanced. The main topics covered are AI Engineering, RAG, vector search, guardrails.
A production AI Agent requires 4 critical components: reactive planning loop (ReAct/OODA), type-safe tool registry with validation, persistent memory...
Build ML microservices with FastAPI (Python 3.12) and Pydantic v2: request/response validation, async/await, lifespan for model loading at boot, dependency injection, health checks, graceful degradation with on-prem Ollama.
Practical guide to prompt engineering techniques for reliable code generation with LLMs. Few-shot learning, chain-of-thought, role prompting, structured output, iteration and evaluation. Real examples with Ollama on-prem.
Sports prediction pipeline with LightGBM in Python: feature engineering, gradient boosting, probabilistic calibration, evaluation with Brier score on a dataset of 2000+ matches.
RAGAS framework, faithfulness/relevance metrics, golden dataset 100 Q&A in Italian, DeepEval, continuous evaluation loop for RAG production.
PagedAttention deep dive, continuous batching, tensor/pipeline parallelism, GPTQ/AWQ quantization, Italian SME deployment patterns on Hetzner/OVH.
vLLM, llama.cpp, TGI comparison vs Ollama: performance, memory overhead, inference latency, quantization support, EU sovereignty constraints for SMEs.
How I built an open source travel intelligence engine with Redis HNSW, sentence-transformers, Cloudflare Workers AI and 8,802 POIs from OSM and Wikidata.
Taxonomy of AI failures: 12 ways your LLM can break You've deployed your AI service. It works. For three weeks everything is fine.
Agents are not chat: autonomous architectures that do more than you ask for An actual agent is an LLM that uses tools, maintains state, fails, and recovers...
Complete guide to RAG Explained: Retrieval-Augmented Generation Fundamentals: architecture, practical implementation and best practices for developers and technical teams.
Provider Failover AI: Ollama → OpenAI → Fallback Static, Case Study Architecture: Provider Failover AI: Ollama → OpenAI → Fallback Static, Case Study...
Observability agent: tracing span-per-thought with OpenTelemetry Your agent failed after 47 tool call. The log says AssertionError: unexpected output...
Complete guide to Embeddings and Semantic Search: Choosing the Right Model: architecture, practical implementation and best practices for developers and technical teams.
Graceful Degradation UX AI: What the User Sees When AI is Down Imagine this scenario: a user opens your portfolio's AI chat, types in a...
Cost optimization agent: reduce token spend 80% without losing quality An agent Claude Sonnet 3.7 without optimizations, on a document analysis task...
Complete guide to Vector Database Comparison: Qdrant, Pinecone, Weaviate: architecture, practical implementation and best practices for developers and technical teams.
Circuit breaker LLM: Resilience4j + smart recovery + half-open probing The circuit breaker is born from electrical mechanics: when the current exceeds...
Debugging agent loop: 6 patterns of deadlock and how to escape them: Debugging agent loop: 6 patterns of deadlock and how to escape them Your agent started with ...,
Complete guide to Hybrid Search: Combining Keyword and Vector Retrieval: architecture, practical implementation and best practices for developers and technical teams.
Tool design for agent: naming, schema, error handling that reduce errors by 60%: Tool design for agent: naming, schema, error handling that reduce...
Complete guide to Building RAG with LangChain: Document Processing and Chains: architecture, practical implementation and best practices for developers and technical teams.
Complete guide to Prompt Engineering at Scale: Techniques and Best Practices: architecture, practical implementation and best practices for developers and technical teams.
Complete guide to Managing LLM Context: Memory Allocation and Optimization: architecture, practical implementation and best practices for developers and technical teams.
Complete guide to Multi-Agent Systems: Orchestration and Coordination Patterns: architecture, practical implementation and best practices for developers and technical teams.
Complete guide to Knowledge Graphs: Building Structured Knowledge for AI: architecture, practical implementation and best practices for developers and technical teams.
Complete guide to Evaluating RAG: Metrics, Benchmarking, Production Monitoring: architecture, practical implementation and best practices for developers and technical teams.
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