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.

Where an instruction belongs — tool description, system prompt, or user turn — why emphasis that worked on older models now causes over-triggering, and the prompt patterns that have expired.

When an action deserves its own tool instead of bash, how to write a description that raises selection accuracy, and how to scale past a few dozen tools without destroying the prompt cache.

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.

RLHF, DPO and RLAIF explained by what each one removes: how preference pairs teach judgement that demonstrations cannot, and why reward hacking and length bias are predictable.

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.

Approval gates for autonomous agents — placing them by reversibility, the confirmation round trip and the deadlocks it hides, denials that redirect rather than stall, and what to do when no human is watching.

Make a long agent run survive a crash: the window where a side effect lands before anything records it, idempotency keys that survive replay, and what to checkpoint.

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%.

Let Claude reason before it answers: adaptive thinking, the five effort levels, why budget_tokens is gone, summarized vs omitted thinking blocks, and preserving them across tool calls.

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.

Manual loop vs the SDK Tool Runner vs Managed Agents vs the Claude Agent SDK — separated by who supplies the agent harness and who supplies the deployment.

Context editing, compaction and memory as Claude API features: what each does to the conversation, and why the compaction block must be appended back verbatim.

Control what an agent costs: effort levels, task budgets vs session budgets vs max_tokens, prompt-cache economics, model routing, and per-turn token accounting.

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.

Run a Claude Agent SDK agent in ten lines: query() vs ClaudeSDKClient, every message type the stream yields, and configuring model, budget and tool restrictions through ClaudeAgentOptions.

Build custom Claude Agent SDK tools with @tool and create_sdk_mcp_server, get the mcp__server__tool naming right, and see four production tools — SQL, HTTP, filesystem, shell — with the guardrail each needs.

Gate a Claude agent: permission modes, disallowed_tools vs allowed_tools, deciding per call with can_use_tool, and using PreToolUse and PostToolUse hooks for audit logging and redaction.

A complete Claude Agent SDK agent: a custom diff tool, a stripped tool surface, a per-call path gate, an audit hook, a reviewer subagent, and a budget cap — structurally unable to modify what it reviews.

Build a LangGraph agent from scratch: StateGraph, TypedDict state, the add_messages reducer, and the decision table for when a graph beats a plain while-loop.

Define LangGraph tools with @tool, wire ToolNode and tools_condition into an agent loop, use the prebuilt create_agent, and write custom routers that branch on your own state.

LangGraph persistence end to end: checkpointers and thread_id for resumable runs, get_state and time travel, PostgresSaver in production, and the Store for memory that outlives a thread.

Pause a LangGraph run for human review with interrupt(), resume it with Command, and build one gate that supports approve, edit and reject without double-charging on re-run.

Multi-agent LangGraph: routing and updating state in one Command return, using a compiled graph as a node, and choosing between shared and isolated history for a worker.

A complete runnable LangGraph agent in sixty lines: two tools, a router that knows which is dangerous, an approval gate on refunds, and a checkpointer so the pause survives a restart.

Deploy a LangGraph agent: API worker vs queue consumer vs scheduled job on one Postgres, stream modes per consumer, bounding steps and tokens, and testing routers without a model.

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

Document processing for RAG pipelines: SFTP and connector ingestion, 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.

Take an agent from a script on your laptop to production, step by step: HTTP service, Docker, secrets, the four runtime shapes, state schema, environments, a CI pipeline with an eval gate, and the first 24 hours live. Explained for non-engineers and staff engineers at once.

The ten layers of a production agent stack — model access, gateway, orchestration, runtime, state, vector, queue, observability, evals, secrets — with the choice criterion for each, three fully costed reference stacks, and the four buy-vs-build calls that matter.

Running multi-agent systems as deployed infrastructure: four runtime topologies compared, the task envelope contract between agents, fleet versioning, distributed tracing across hops, retry-storm math, and a four-agent contract-review pipeline built end to end.

Why CPU autoscaling is the wrong signal for a 40-second agent run: in-flight-run scaling, queueing-theory capacity math, the derived timeout ladder, connection pooling, a five-rung degradation ladder, token-rate limiting, and a load-test recipe.

Keeping an agent alive after launch: the six drift sources, SLOs that measure the product instead of the server, the maintenance calendar, incident response for silent quality failures, runbooks you can run at 3am, the model-deprecation migration, and cost governance.

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.

One end-to-end interview playbook for AI engineers: the coding bar, a six-step process, the seven discovery dimensions, communication structure, scripts to say out loud, recovery lines, and four worked examples.