Claude and AI resources.
Lightbridge Labs publishes these guides as an independent, practitioner starting point for teams adopting Claude and other AI systems. We build and operate production AI, so the explanations describe the tools as they actually work, not as a vendor would frame them. Start with which product fits, then go deeper.
Every resource is written by senior Lightbridge practitioners, most with two decades of experience in industry and consulting, and grounded in hands-on enterprise engagements since 2017.
What is Claude?
Claude is the AI assistant family built by Anthropic. Understand the chat assistant, its model tiers, where to use it, and how it fits the wider lineup.
Read the guide →What is generative AI?
Generative AI creates new text, images, code, and audio from learned patterns. Understand how it works, the model types, business uses, and the limits leaders must manage.
Read the guide →What is agentic AI?
Agentic AI is the paradigm of AI that plans, acts through tools, and pursues goals on its own. Where it creates value, the risks, and how to adopt it responsibly.
Read the guide →AI agents vs agentic AI
An AI agent is the unit; agentic AI is the paradigm it belongs to. Where a multi-agent system fits, a single-agent-versus-multi-agent comparison, and when each applies.
Read the guide →Which Claude product should you use?
Confused by claude.ai, Cowork, Claude Code, the API, Design, Security, and Claude on AWS, Google, and Azure? A vendor-neutral guide to picking the right Claude surface.
Read the guide →Claude vs ChatGPT
Claude vs ChatGPT, compared fairly by Lightbridge Labs: what each is, where each shines, the model families, enterprise posture, and how to pick the right assistant.
Read the guide →Claude vs Gemini
Claude vs Gemini compared fairly: what each is, model families, strengths, enterprise posture, and where each runs. A balanced read from Lightbridge Labs.
Read the guide →What is Claude Cowork?
Claude Cowork is the Anthropic agentic desktop app for knowledge workers. It works inside folders you grant, completes multi-step file tasks, and returns drafts for review.
Read the guide →What is Claude Code?
Claude Code is the Anthropic AI coding agent. Learn its surfaces, how CLAUDE.md carries project context, and how skills, hooks, and plugins extend it for teams.
Read the guide →Building with the Claude API
A developer guide to building on the Claude API: single calls, streaming, tool use, agents, MCP, and the production concerns of cost, latency, and evaluation.
Read the guide →What is RAG?
RAG, or retrieval-augmented generation, lets a model answer from external context retrieved at query time. How it works, how it differs from fine-tuning, and where it fits.
Read the guide →What is an AI agent?
A plain guide to agentic AI: how an agent differs from a chatbot, its building blocks, where agents fit, where to avoid them, and how to deploy them safely.
Read the guide →How to build an AI agent
A practical build guide: the agent loop, scoping the first agent, choosing tools, the vendor-neutral framework landscape, evaluation, guardrails, and production.
Read the guide →What is the Model Context Protocol (MCP)?
The open standard, introduced by Anthropic in 2024, that connects AI models to external tools and data through one interface: the architecture, the tools, resources, and prompts primitives, and why it matters.
Read the guide →AI agent tools and platforms landscape
A vendor-neutral map of the AI agent tooling space by category: frameworks and SDKs, no-code builders, orchestration, integration layers including MCP, evaluation, and guardrails, with how to choose in each.
Read the guide →What are multi-agent systems?
Several AI agents, each with a role, that coordinate on a goal one agent could not. Coordination patterns, the autonomy spectrum from human-in-the-loop to autonomous, and the added risks.
Read the guide →What are agentic workflows?
AI-driven processes where a model drives multi-step work. The workflow-versus-agent distinction, the common patterns, agentic workflows versus traditional automation, and when to use each.
Read the guide →What is context engineering?
Designing what an AI model sees at inference time: system prompts, retrieval, tool outputs, and memory. How it extends prompt engineering, the core techniques, and the common failure modes.
Read the guide →Enterprise AI use cases
Where AI creates value across the enterprise, organized by business function, plus a method for choosing the few use cases worth funding first.
Read the guide →AI ROI: how to measure the return on AI
Why AI ROI is hard to measure, the value drivers where it shows up, a practical way to model it before you build, and build-versus-buy as an ROI lever. A decision-stage guide.
Read the guide →How to prompt Claude
A free primer on prompting Claude: the five-part mental model, prompt structure, format control, ready-to-use templates, and why identical prompts give different answers.
Read the guide →Claude for Google Workspace
Connect Claude to Google Sheets, Docs, and Slides. How the integration reads and writes each app, the tool-use and MCP patterns, the auth model, and a safe security posture.
Read the guide →Claude security and compliance
The three ways to consume Claude, Amazon Bedrock, the Anthropic API, and Claude.ai Enterprise, compared on data handling and mapped to SOC 2, ISO 27001, SOX, and GDPR.
Read the guide →EU AI Act compliance guide
The EU AI Act explained: the risk tiers, who it covers including non-EU firms, the core obligations, the phased rollout, and how ISO 42001 supports readiness.
Read the guide →ISO 42001 compliance guide
ISO 42001 is the international standard for AI management systems. What an AIMS is, the standard structure, who needs it, and the certification path, explained.
Read the guide →AI governance framework
What an AI governance framework is, the components that make one work, the reference frameworks to build on, and a staged path to stand one up.
Read the guide →What is an AI policy?
The governing document for how an organization uses AI: what it contains, how it fits inside an AI management system, how to write one, and what a strong AI policy template covers.
Read the guide →What is an AI audit?
A structured assessment of an AI system or program against defined criteria. The types, how an AI risk assessment relates, how to run one, and the standards that anchor it.
Read the guide →NIST AI Risk Management Framework
The NIST AI RMF explained: its four functions, the trustworthy-AI characteristics, its voluntary status, and the companion Playbook and Generative AI Profile.
Read the guide →AI and Claude glossary
A plain-language glossary of AI and Claude terms: LLMs, RAG, agents, MCP, prompt engineering, ISO 42001, and AI governance, defined clearly by Lightbridge Labs.
Read the guide →Lightbridge Labs explains AI tools without selling a platform.
Most explainers of AI products are published by the vendors who make them, so the framing tilts toward what they sell. Lightbridge Labs is an independent AI consulting and training practice. These guides name products neutrally, describe tradeoffs honestly, and point to the canonical vendor documentation for anything that changes quickly.
When you are ready to turn understanding into a deployment, the same practice delivers custom AI development, AI strategy, and AI governance, plus hands-on corporate AI training.
From understanding Claude to putting it into production.
When the reading is done and a real deployment is on the table, Lightbridge Labs helps you build, govern, and train your teams on production AI.