·Artificial Intelligence
Capstone Project: The AI Architect
It's time to put it all together. In this final Capstone Project, you will design a high-stakes AI application from scratch, applying every concept from the 12 modules of this course.
engineering
127 articles
·AI Agents and LLMs
Agent vs Traditional Microservices
Is an agent just a specialized microservice? Explore the fundamental differences in control flow, observability, and reliability between agents and traditional services.
·AI Agents and LLMs
Cost Engineering for AI Systems
AI tokens are the new cloud bill. Learn how to optimize your AI costs through semantic caching, model routing, and prompt compression.
·AI Agents and LLMs
The Future of Developer Work in an AI-First World
Is AI coming for your job? Explore how the role of the software engineer is evolving from writing syntax to orchestrating intelligent systems.
·AI Agents and LLMs
Building AI Products With Human-in-the-Loop
Automation is not all-or-nothing. Learn how to design effective Human-in-the-Loop (HITL) systems that combine AI speed with human judgment.
·AI Agents and LLMs
Responsible AI for Builders, Not Policymakers
Stop talking about ethics and start building with safety. Learn the practical engineering guardrails, audit trails, and logging strategies for responsible AI.
·AI Agents and LLMs
From APIs to Platforms: The New AI Stack
The AI tech stack is moving beyond the OpenAI API. Explore the layers of the modern AI platform: vector stores, orchestration, and specialized deployment.
·AI Agents and LLMs
Why Most AI Proofs of Concept Fail in Production
Moving from a prototype to production is the hardest part of AI. Explore the 4 major killers of AI PoCs: data quality, cost, latency, and governance.
·AI Security
Module 22 Lesson 1: The AI Security Professional
Defining the role. A deep dive into the day-to-day responsibilities, toolsets, and team dynamics of a professional AI Security Engineer.
·AI Agents and LLMs
AI Security Risks Every Engineer Should Know
AI is the new attack surface. Learn about prompt injection, data leakage, and model misuse, and how to build production-grade security for your AI systems.
·Generative AI
Module 3 Lesson 3: System Prompts
Defining Persona and Role. How to create an 'Invisible' layer of instructions that controls the AI's personality and safety.
·LangChain
Capstone Project: The Autonomous Market Analyst
The Final Challenge. Build a production-ready, agentic RAG system that analyzes companies and returns structured research reports via a REST API.
·LangChain
Module 1 Lesson 1: Python and Virtual Environments
The starting point. How to set up a clean, isolated Python environment for your LangChain projects.
·LangChain
Module 1 Lesson 3: Installing Model Providers
Connecting to the brains. How to install specialized packages for OpenAI, Anthropic, and local model providers.
·LangChain
Module 1 Lesson 4: API Keys and Environment Variables
Security First. How to securely manage your API keys using .env files and prevent accidental leaks.
·LangChain
Module 1 Wrap-up: Verification and First Call
Hands-on: Verify your complete installation and take the Module 1 challenge.
·LangChain
Module 10 Lesson 2: The Agent Loop and Tool Selection
How the Agent decides. Deep dive into the mechanics of tool selection and processing tool outputs.
·LangChain
Module 10 Wrap-up: Building Your First Agent
Hands-on: Combine tools, memory, and reasoning into a single autonomous Research Agent.
·LangChain
Module 11 Wrap-up: Getting Reliable Data
Hands-on: Build an Information Extraction agent that converts raw text into a clean Python object.
·LangChain
Module 12 Lesson 2: Implementing Callbacks in Chains
Connecting the Hearths. How to pass your custom handlers to models, tools, and executors to start capturing events.
·LangChain
Module 12 Wrap-up: Inspecting the Machine
Hands-on: Build a Cost-Monitoring Callback that calculates and prints the price of every AI request.
·LangChain
Module 13 Lesson 1: Debugging Chains
Finding the Bug. Techniques and tools for identifying where a multi-step chain is failing or hallucinating.
·LangChain
Module 13 Lesson 2: LangSmith Overview
The Cloud Control Room. Introduction to LangSmith, the enterprise platform for tracing and optimizing your AI applications.
·LangChain
Module 13 Wrap-up: Professional Observability
Hands-on: Set up LangSmith and trace a complex multi-tool agentic decision.
·LangChain
Module 14 Lesson 1: API Design with FastAPI
Connecting to the Frontend. How to wrap your LangChain apps in a professional REST API using FastAPI.
·LangChain
Module 15 Lesson 1: Packaging LangChain Apps
From Script to Service. How to organize your code and dependencies for reliable deployment on any server.
·LangChain
Module 2 Lesson 1: What is a Model Abstraction?
The Power of Agnosticism. Why LangChain uses wrappers to ensure you can switch models without rewriting your code.
·LangChain
Module 2 Lesson 2: Chat vs. Completion Models
Messages vs. Strings. Understanding the different ways LLMs process input and why Chat models are the modern standard.
·LangChain
Module 2 Lesson 5: Provider-Agnostic Setup
Hands-on: Implementing a model switcher that allows you to change the 'Brain' of your app via a simple configuration.
·LangChain
Module 2 Wrap-up: Designing the Brain
Reviewing model abstractions, streaming, and batching. Your final check before moving to Prompts.
·LangChain
Module 3 Lesson 1: Prompt Fundamentals
The Art of Instruction. Learning the basic principles of prompt engineering: Context, Task, and Format.
·LangChain
Module 3 Lesson 2: PromptTemplate (String Abstraction)
From Static to Dynamic. Using PromptTemplate to create reusable instructions with variables.
·LangChain
Module 3 Lesson 3: ChatPromptTemplate (Messaging Abstraction)
The Agent's Blueprint. How to create templates for multi-role conversations (System, Human, AI).
·LangChain
Module 3 Lesson 4: Few-Shot Prompting
Leading by Example. How to provide the model with sample Q&A pairs to enforce style and accuracy.
·LangChain
Module 3 Lesson 5: Prompt Versioning and the Hub
Prompts as Code. How to version-control your instructions and use the LangChain Hub to share and pull best-in-class prompts.
·LangChain
Module 3 Wrap-up: Your Reusable Library
Hands-on: Build a reusable prompt library and implement a few-shot dynamic classifier.
·LangChain
Module 4 Lesson 2: Single-Step Chains (LCEL Basics)
Piping the Brain. Learning how to use the | operator to create your first executable LangChain Expression Language chain.
·LangChain
Module 4 Lesson 3: Sequential Chains
Multi-Step reasoning. How to pipe the output of one LLM call directly into the input of a second LLM call.
·LangChain
Module 4 Lesson 4: Router Chains
Dynamic Decision Making. How to use an LLM to decide which sub-chain should handle a specific user request.
·LangChain
Module 4 Lesson 5: Chain Composition and Aggregation
Parallel Reasoning. How to run multiple chains at the same time and combine their results into a final synthesized answer.
·LangChain
Module 5 Lesson 3: Text Splitters and Chunking
Optimizing for Logic. Why we must split long documents into smaller 'Chunks' to fit within LLM context windows.
·LangChain
Module 5 Lesson 4: Special Case Splitters (Code & Markdown)
Context-Aware Splitting. How to split Python code by functions and Markdown by headers to maintain semantic integrity.
·LangChain
Module 6 Lesson 2: Embedding Providers (Cloud vs. Local)
Choosing your engine. Comparing OpenAI cloud embeddings with local HuggingFace models for speed and privacy.
·LangChain
Module 6 Lesson 4: Similarity Search and k-Values
Fine-Tuning Retrieval. Learning how to control how many results (k) your vector store returns and what 'Score' means.
·LangChain
Module 7 Lesson 2: The Retriever Interface
The Search Object. How LangChain standardizes vector store lookups into a 'Retriever' that can be used in any chain.
·LangChain
Module 7 Lesson 3: Building the RAG Chain
Piping Facts. Putting it all together into a single LCEL chain that retrieves context and generates an answer.
·LangChain
Module 7 Lesson 4: Designing RAG Prompts
The Art of Grounding. How to write the perfect system prompt to ensure your AI stays factual and cites its sources.
·LangChain
Module 7 Wrap-up: Building a Q&A Bot
Hands-on: Finalize your first production-ready RAG system over your own local documents.
·LangChain
Module 8 Lesson 2: ConversationBufferMemory
The Raw Transcript. Using the simplest memory type to keep a literal record of every message in a conversation.
·LangChain
Module 8 Lesson 3: Summary Memory
Dense context. How to use an LLM to periodically summarize a conversation to keep the memory footprint small.
·LangChain
Module 8 Lesson 4: External Memory (Redis & Postgres)
Production State. How to move your memory from local RAM to persistent databases for multi-user applications.
·LangChain
Module 8 Wrap-up: Conversations that Stick
Hands-on: Build a persistent chatbot that remembers your name across different CLI sessions.
·LangChain
Module 9 Lesson 2: Defining Custom Tools (The @tool Decorator)
Creating Superpowers. How to turn any Python function into a LangChain tool using a simple decorator.
·LangChain
Module 9 Wrap-up: Giving Your Agent Powers
Hands-on: Build a toolbox for an agent that can multiply numbers and search Wikipedia.
·AI Agents and LLMs
LLMs Beyond Chat: Real Business Use Cases
Chatbots are just the entry point. Discover how enterprises are using Large Language Models for automated search, summarization, and complex decision support.
·AI Agents and LLMs
The Agentic Shift: How AI Agents Are Redefining Software Architecture
Move beyond simple chat interfaces. Explore how autonomous AI agents are transforming software design from static code to dynamic, self-optimizing systems.
·Agentic AI
Module 1 Lesson 1: What Agentic AI Is
The shift from generative to agentic. Understanding the third wave of AI development.
·Agentic AI
Module 1 Lesson 4: Deterministic vs Non-Deterministic Systems
Stability in the storm. Managing the unpredictability of LLMs within structured software environments.
·Agentic AI
Module 1 Lesson 5: When You Should Not Use Agents
Avoiding the 'Everything is a Nail' trap. Understanding the cost, latency, and reliability trade-offs.
·Agentic AI
Module 10 Lesson 1: What ADK Solves
Standardizing the agent factory. Understanding the need for enterprise-grade management, configuration, and policies.
·Agentic AI
Module 10 Lesson 2: Agent Lifecycle Management
From cradle to grave. Managing versioning, rolling updates, and retirement of AI agents.
·Agentic AI
Module 10 Lesson 3: Configuration-Driven Agents
Separating logic from code. Using YAML and JSON to define agent personas, tools, and constraints.
·Agentic AI
Module 11 Lesson 2: When to use LangChain
The Speed Demon. Understanding the scenarios where LangChain's core abstractions outshine more complex frameworks.
·Agentic AI
Module 11 Lesson 3: When to use LangGraph
The Enterprise Choice. Why LangGraph is the standard for high-reliability, long-running agent systems.
·Agentic AI
Module 12 Lesson 1: Why Agents Hallucinate
Understanding the glitch. The psychological and technical causes of AI hallucinations in agentic systems.
·Agentic AI
Module 12 Lesson 2: Prompt Engineering for Accuracy
Words that matter. Advanced prompting techniques like Chain-of-Thought and Few-Shot that reduce agentic errors by 40%.
·Agentic AI
Module 12 Lesson 3: Output Parsing and Validation
Structured safety. Using Pydantic and JSON schemas to ensure the agent's output is machine-readable and error-free.
·Agentic AI
Module 12 Lesson 4: Self-Correction Loops
The double-check. Implementing internal loops where one agent reviews and corrects the errors of another.
·Agentic AI
Module 12 Lesson 5: Benchmarking Agent Performance
Measuring progress. How to build an Evals suite to test your agent's accuracy, tool usage, and cost over time.
·Agentic AI
Module 13 Lesson 1: Prompt Injection Attacks
Hacking the brain. Understanding how users can trick your agent into bypassing its own rules.
·Agentic AI
Module 13 Lesson 2: Data Privacy and PII
Protecting the user. How to handle Personally Identifiable Information (PII) and ensure your agent is GDPR/CCPA compliant.
·Agentic AI
Module 13 Lesson 4: Rate Limiting and Cost Guards
Economic security. Preventing runaway costs and protecting your API keys from exhaustion.
·Agentic AI
Module 14 Lesson 1: The Reliability Gap
Why LLMs aren't enough. Understanding the limit of probabilistic reasoning in deterministic business systems.
·Agentic AI
Module 14 Lesson 2: Finite State Machines (FSMs)
Old school intelligence. Learning how to define states, transitions, and triggers to create unshakeable logic.
·Agentic AI
Module 14 Lesson 3: Using LLMs as State Transitions
The bridge. How to use an LLM to classify user intent and trigger a specific state change in your FSM.
·Agentic AI
Module 14 Lesson 4: Constraining Output with Outlines
Mathematically impossible to fail. Using libraries like Outlines and Guidance to force the LLM to follow a specific grammar or regex.
·Agentic AI
Module 14 Lesson 5: FSMs in Customer Service
The support architect. Designing an automated ticket system that uses an FSM for policy and an LLM for empathy.
·Agentic AI
Module 15 Lesson 1: Vector Databases for Agents
The Long-Term Memory. Learning how to use Pinecone, Chroma, and PGVector to give your agents a massive knowledge base.
·Agentic AI
Module 15 Lesson 2: Semantic Caching
Save time, save money. Using GPTCache to avoid calling the expensive LLM for identical (or similar) queries.
·Agentic AI
Module 15 Lesson 3: Monitoring and Logs
The eyes of the system. Implementing real-time tracking for agent health, token usage, and user sentiment.
·Agentic AI
Module 15 Lesson 4: Scaling Agent Concurrency
Handling the crowd. How to manage thousands of concurrent agents without crashing your database or hit API limits.
·Agentic AI
Module 15 Lesson 5: CI/CD for Agents
Pipelining intelligence. How to automate the testing, benchmarking, and deployment of your agentic code and prompts.
·Agentic AI
Module 16 Lesson 1: The Chat Box is Not Enough
Breaking the box. Why traditional chat interfaces fail to communicate the complexity of multi-agent systems.
·Agentic AI
Module 16 Lesson 2: Generative UI
Beyond text. How to use Vercel AI SDK and specialized tokens to render interactive UI components (dashboards, charts) on the fly.
·Agentic AI
Module 2 Lesson 1: Agent Components
Dissecting the agent. Understanding the four pillars: LLM, Memory, Tools, and the Control Loop.
·Agentic AI
Module 2 Lesson 2: Agent State
The snapshot of intelligence. Managing variables, message history, and flags within a running agent.
·Agentic AI
Module 2 Lesson 4: Tool Invocation Basics
The mechanics of action. How LLMs trigger external functions using structured JSON.
·Agentic AI
Module 2 Lesson 5: Feedback Loops
Self-correcting AI. How agents learn from errors and retry failed attempts during execution.
·Agentic AI
Module 3 Lesson 1: The ReAct Pattern
Mastering the fundamental agent loop. How to prompt for Thought, Action, and Observation.
·Agentic AI
Module 3 Lesson 2: The Planner–Executor Pattern
Divide and conquer. Using one agent to build the map and another to walk the path.
·Agentic AI
Module 4 Lesson 1: Agents in LangChain
The framework of choice. Understanding the high-level Agent abstractions in LangChain.
·Agentic AI
Module 4 Lesson 3: Agent Executors
The heartbeat of LangChain agents. Managing the while-loop and handling the ReAct cycle.
·Agentic AI
Module 4 Lesson 4: Memory Integration
Connecting the threads. How to implement chat history and windowed memory in LangChain agents.
·Agentic AI
Module 4 Lesson 5: Agent Failure Modes
Why agents break. Understanding hallucinations, tool loops, and parsing errors in LangChain.
·Agentic AI
Module 5 Lesson 1: Infinite Loops
The AI feedback trap. Why agents get stuck in repetitive cycles and how to break them.
·Agentic AI
Module 5 Lesson 2: Tool Abuse
When models get lazy. Understanding how agents take shortcuts or misuse tools to avoid difficult reasoning.
·Agentic AI
Module 5 Lesson 3: Hallucinated Actions
Ghost tools. How to handle agents that try to execute functions that don't exist in their toolbox.
·Agentic AI
Module 5 Lesson 4: Debugging Difficulty
The Black Box problem. Why traditional debuggers fail and how to use traces to find the glitch.
·Agentic AI
Module 5 Lesson 5: Why Control Flow Matters
From magic to structure. Why giving an agent too much freedom is a recipes for disaster.
·Agentic AI
Module 6 Lesson 1: Why LangGraph Exists
The successor to the Loop. Understanding the need for cyclic graphs in agent development.
·Agentic AI
Module 6 Lesson 2: Graph-Based Agent Architecture
Designing the flowchart of intelligence. Understanding nodes, edges, and state transitions.
·Agentic AI
Module 6 Lesson 3: Nodes, Edges, and State
The building blocks of LangGraph. Learning how to write functions that play nice with the graph schema.
·Agentic AI
Module 7 Lesson 1: Conditional Routing
The logic of the graph. Mastering how to route control based on tool calls, state variables, and LLM output.
·Agentic AI
Module 7 Lesson 4: Validation Nodes
The zero-trust agent. How to implement verification steps that ensure output quality before finishing.
·Agentic AI
Module 7 Wrap-up: The Master of Graphs
Hands-on: Build a complex research agent with routing, loop guards, and a validation gate.
·Agentic AI
Module 8 Lesson 1: What CrewAI Is
From single actors to ensemble casts. Understanding the specialized role-playing framework for multi-agent systems.
·Agentic AI
Module 8 Lesson 2: Roles and Responsibilities
Designing the persona. How to write effective Roles, Goals, and Backstories to maximize agent performance.
·Agentic AI
Module 8 Lesson 3: Task Decomposition
Breaking it down. How to turn a complex project into a set of discrete, delegated Tasks in CrewAI.
·Agentic AI
Module 8 Lesson 5: Communication Patterns
How agents share data. Understanding context passing, memory sharing, and the 'Kitchen' environment in CrewAI.
·Agentic AI
Module 9 Lesson 2: Event-Driven Agent Design
Reacting in real-time. How to design agents that trigger actions based on external signals rather than internal loops.
·Agentic AI
Module 9 Lesson 5: When StrandAgents Make Sense
Choosing the right tool. Understanding when to use event-driven strands over complex graphs or crews.
·AI & LLMs
Module 11 Lesson 1: When Fine-Tuning Is Needed
RAG vs Fine-Tuning. Knowing when to give the AI a book and when to perform surgery on its brain.
·AI & LLMs
Module 12 Lesson 1: Local AI Security Model
Trust but verify. Understanding the security boundaries of the Ollama server and how to protect your API.
·AI & LLMs
Module 12 Lesson 2: Data Privacy and Anonymization
Protecting the prompt. How to ensure sensitive user data like PII doesn't end up in your AI logs.
·AI & LLMs
Module 14 Lesson 2: Hardware Selection for Production
Buying for the future. A guide to RAM, VRAM, and processing power for high-uptime AI applications.
·AI & LLMs
Module 2 Lesson 2: Ollama Architecture
How Ollama works under the hood. Understanding the service, the CLI, and the llama.cpp engine.
·AI & LLMs
Module 3 Lesson 5: Prompting Local Models
Talking to the machine. Why prompting a local 8B model requires a different approach than ChatGPT.
·AI & LLMs
Module 4 Lesson 3: GGUF Model Format
The universal file type. Why GGUF is the 'PDF of AI' and why it's the foundation of the Ollama ecosystem.
·AI & LLMs
Module 4 Lesson 5: Tokenization
The bridge between words and numbers. How LLMs translate your typing into something a computer can process.
·AI & LLMs
Module 4 Lesson 6: Performance Trade-offs
Optimization 101. Balancing speed vs quality vs memory in your local AI setup.
·AI & LLMs
Module 5 Lesson 4: Runtime Parameters
Fine-tuning the engine. A dictionary of PARAMETER options to control speed, creativity, and memory.
·AI & LLMs
Module 6 Lesson 3: Supported Architectures
Not all models are equal. Understanding which architectures (Llama, Mistral, BERT) work with the Ollama engine.
·AI & LLMs
Module 6 Lesson 5: Quantization Options
Going deep on compression. Exploring the technical differences between Q4_0, Q4_K_M, and GQA.
·AI & LLMs
Module 9 Lesson 1: Prompt Design for Smaller Models
Optimization for 8B. Why 'Chain of Thought' is the secret weapon for making small models act like giants.
·AI & LLMs
Module 9 Lesson 2: Advanced System Prompts
Hardening the persona. Using system prompts as a defensive layer to prevent 'Jailbreaking' and off-topic conversations.
·AI & LLMs
Module 9 Lesson 3: Output Control
Precision generation. Techniques to limit the model's verbosity and ensure it stays within character limits.
·AI & LLMs
Module 9 Lesson 5: Reducing Hallucinations
Stick to the facts. Techniques to prevent local AI from making up information.