Explore AI Engineering Topics

Complete lifecycle of large language models from pre-training through fine-tuning, RLHF, and deployment — with architecture diagrams and production considerations.

How LLMs use function calling and tool use — the mechanics behind tool-calling agents, from prompt engineering to structured output.

Should you fine-tune at all? A structured decision framework for prompt engineering vs RAG vs fine-tuning.

How LoRA works inside transformer layers, QLoRA for memory-efficient training, and the full PEFT method comparison with code examples.

Train a small, fast model to mimic a large teacher — economics, pipeline, and quality filters for production distillation.

Data volume guidelines, quality checklists, and the complete preparation pipeline for fine-tuning datasets.

The economics of fine-tuning — quality/cost/latency triangle, real 2026 price points, break-even math, and the maintenance tax nobody budgets for.

How to evaluate fine-tuned models — metrics by task type, regression testing, and the complete evaluation pipeline.

Step-by-step breakdown of an LLM tool call: request, schema validation, execution, and result handling with code examples.

Stateless vs stateful LLM architectures: trade-offs for agent design, conversation management, and production deployment.

Build a complete tool-calling AI agent in 15 lines of Python. Understand the core agent loop pattern that powers all LLM agents.

The spectrum of AI agent architectures from simple prompt-response to fully autonomous multi-agent systems, with trade-offs at each level.

ReAct (Reasoning + Acting) pattern for AI agents: how to combine chain-of-thought reasoning with tool use for better agent performance.

Reflexion pattern for self-improving AI agents: verbal reinforcement learning where agents turn failures into written lessons and retry — no gradients required.

Hierarchical delegation pattern for multi-agent systems: orchestrating specialized agents with a coordinator for complex tasks.

Planner-Executor agent pattern: separating planning and execution phases for more reliable and debuggable AI agent workflows.

Working, short-term, long-term, and org-wide memory — what each type stores, its lifetime, and implementation.

Production-ready memory code, episodic vs semantic vs procedural memory, and system design patterns.

When to use which memory type, with three real-world scenarios: customer support, sales agent, and code assistant.

GDPR and privacy compliance for agent memory, plus FDE interview scenarios and deep-dive questions.

Claude Messages API deep dive: request/response format, system prompts, multi-turn conversations, and best practices.

Implement tool use with Claude API: define tools, handle tool calls, and build reliable function-calling agents.

Stream Claude API responses for real-time UX: server-sent events, token-by-token rendering, and production streaming patterns.

Get structured JSON output from Claude: constrained generation, schema validation, and reliable data extraction patterns.

Cut Claude cost and latency by up to 90% with prompt caching: cache the stable prefix (system prompt, tools, documents, history), pay full price once, then read at ~10%.

Give Claude a reasoning scratchpad before it answers: thinking budgets, thinking blocks with tool use, interleaved thinking, and when the cost/quality trade-off is worth it.

Production patterns for building AI agents with the Claude Agent SDK: the loop, subagents, permissions, context management, MCP, and hooks — plus when to use the SDK vs the raw API.

Observability metrics for LLM applications: latency, token usage, cost tracking, and quality scoring dashboards.

Complete guide to LLM evaluation and observability: automated evals, human feedback loops, A/B testing, and monitoring.

Evaluate AI agents in production: task completion metrics, trajectory analysis, and automated agent quality benchmarks.

Retrieval-Augmented Generation (RAG) architecture explained: ingestion pipeline, vector search, prompt augmentation, and production patterns.

Document processing for RAG pipelines: PDF parsing, OCR, table extraction, and multi-modal document understanding.

Text chunking strategies for RAG: fixed-size, semantic, recursive, and document-aware chunking with performance comparisons.

Embedding models and vector indexing for RAG: choosing embeddings, HNSW vs IVF, dimensionality, and index optimization.

Understanding embeddings for AI applications: text, image, and multi-modal embeddings with similarity search and clustering.

Metadata strategies for RAG: filtering, hybrid search, metadata extraction, and structured metadata for improved retrieval.

Advanced retrieval and reranking for RAG: BM25, dense retrieval, cross-encoder reranking, and hybrid search strategies.

Evaluate RAG system quality: retrieval precision/recall, answer faithfulness, and end-to-end pipeline benchmarking.

RAGAS evaluation framework for RAG: faithfulness, answer relevancy, context precision, and automated quality scoring.

Production monitoring for RAG systems: retrieval quality dashboards, drift detection, and automated alerting.

Advanced RAG techniques: query decomposition, self-RAG, corrective RAG, adaptive retrieval, and multi-hop reasoning.

Production RAG best practices: pipeline optimization, failure handling, testing strategies, and common pitfalls to avoid.

Model Context Protocol (MCP) explained: the open standard for connecting AI models to tools, data sources, and external systems.

MCP architecture deep dive: client-server model, protocol layers, message types, and connection lifecycle.

Build MCP servers step-by-step: Python and TypeScript implementations with tools, resources, and prompts.

MCP transport layers: stdio, SSE, and streamable HTTP transports with implementation details and trade-offs.

MCP tool discovery and capability negotiation: how clients discover server capabilities and tools dynamically.

Security considerations for MCP: authentication, authorization, input validation, and sandboxing strategies.

Deploy MCP servers in production: scaling, monitoring, error handling, and reliability patterns.

Run MCP on Google Cloud Platform: Cloud Run deployment, IAM integration, and GCP-native tool implementations.

System design fundamentals for AI engineers: client-server, APIs, latency percentiles, caching, load balancing, databases, and queues — each explained from zero, then mapped to how LLM systems change it.

The 60-term plain-English glossary for AI system design: LLM basics, retrieval, agents, infrastructure, reliability, scaling, cost, and safety — with deep-dive links into every guide section.

Build a support bot end to end: six iterations from one API call to a production-shaped architecture with retrieval, caching, model routing, guardrails, and observability — runnable code at every step.

Traditional vs agentic system design: the 7 dimensions that transform, anatomy of an agentic system, control flow paradigms, failure modes, and when to go agentic.

Five-phase system design framework for AI interviews: requirements, architecture, data flow, scaling, and production readiness.

Staff-level 10-layer architecture for AI-native systems: from infrastructure to user experience, with production examples.

Scale AI systems from 10K to 1M users: caching, sharding, async processing, and infrastructure evolution strategies.

How OpenAI Deep Research, Claude Code, Perplexity, Cursor, and Devin work under the hood. Production architecture breakdowns with design decisions for interviews.

Security and privacy for AI applications: threat models, data protection, compliance frameworks, and defense-in-depth.

AI guardrails and safety: content filtering, output validation, safety classifiers, and responsible AI deployment.

PII detection and redaction in LLM applications: entity recognition, masking strategies, and compliance automation.

Defend against prompt injection attacks: detection techniques, input sanitization, and multi-layer defense strategies.

Multi-tenant isolation for AI platforms: data separation, model isolation, rate limiting, and tenant-aware architectures.

Audit logging and compliance for AI systems: SOC2, HIPAA, GDPR requirements, and automated compliance monitoring.

The discipline replacing prompt engineering: designing dynamic context systems that give agents the right information at the right time. Covers lazy loading, memory-augmented architectures, and production patterns.

Semantic caching for LLM applications: reduce costs and latency by caching semantically similar queries with vector similarity.

Tiered model routing: route queries to the right model (GPT-4, Claude, Haiku) based on complexity, cost, and latency requirements.

Rate limiting and cost management for LLM APIs: token budgets, per-user quotas, and cost optimization strategies.

LLM inference optimization: batching, quantization, KV-cache, speculative decoding, and hardware selection.

Detect and prevent LLM hallucinations: factuality checking, grounding verification, and off-brand content filtering.

Common AI agent failure modes: infinite loops, tool misuse, context window overflow, and recovery strategies.

Deploy and roll out AI systems: canary releases, feature flags, A/B testing, and safe rollback strategies.

Event-driven async architectures for AI: message queues, webhook patterns, and asynchronous agent orchestration.

Build a data flywheel for AI products: feedback loops, continuous learning, and data-driven model improvement cycles.

Claude Agent SDK patterns: building production multi-agent systems with guardrails, handoffs, and tool orchestration.

ADRs for AI systems: a reusable template plus three fully worked records (model provider choice, build-vs-buy guardrails, sync-to-async migration) — each with an explicit revisit trigger.

Staff-level AI migration playbooks: provider swaps, monolith-to-agentic decomposition, model-version upgrades, and zero-downtime component swaps under one safe shadow → canary → ramp → rollback pattern.

The staff organizational layer: platform-vs-product ownership, incident command for silent LLM-quality failures, cost governance, the RFC process, and driving adoption without authority.

LLM inference cost vs latency trade-offs: optimization strategies for production AI systems with budget constraints.

Prevent code and data leakage in LLM applications: sandboxing, output filtering, and secure coding practices.

AI engineering interview format and evaluation rubrics: what interviewers look for and how to structure your responses.

Six-step process for acing AI engineering interviews: from clarification to trade-off analysis with real examples.

Discovery questions framework for AI interviews: how to ask the right clarifying questions before designing a system.

Communication structure for technical interviews: how to articulate your thinking clearly and concisely under pressure.

Common interview pitfalls and recovery strategies: how to handle mistakes, blanking, and difficult follow-up questions.

Pythonic idioms and patterns every AI engineer should know: comprehensions, generators, context managers, and clean code.

Python data structures for coding interviews: lists, dicts, sets, heaps, deques, and their time complexity trade-offs.

Quick guide to async Python: asyncio basics, await patterns, and concurrent task execution for AI applications.

Complete async Python guide: event loops, coroutines, task groups, semaphores, and production async patterns.

Python collections module deep dive: Counter, defaultdict, OrderedDict, namedtuple, and deque with interview examples.