Understanding LLMs & Choosing Your AI Stack
Before you build anything with AI, you need to understand what you're actually working with. This module covers how large language models work (in plain English), how the major models compare, and the key concepts - projects, skills, MCPs, direct connections - that will shape every decision you make going forward.
- How LLMs generate output and why they hallucinate
- How Claude, ChatGPT, and Gemini differ across 9 dimensions
- The difference between Projects, Skills, and MCPs
- Choose the right AI model for any given task
- Set up a Claude Project that makes every conversation 10× more effective
- Decide whether to build an MCP or use a direct connection
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Understanding LLMs & Choosing Your AI Stack lesson
1. How LLMs actually work (plain English)
A large language model is not a search engine, a database, or a calculator. It's a pattern-completion system trained on an enormous amount of text. Given an input, it predicts the most statistically likely continuation - shaped by billions of parameters learned during training.
What this means practically for a PM:
- It has no memory between conversations unless you give it one
- It doesn't "know" things the way a database does - it has learned patterns and associations
- It can be confidently wrong (hallucination) because it optimises for plausible, not true
- The quality of its output is heavily shaped by the quality and structure of its input
- It reasons better when given time to think (chain-of-thought) vs asked for instant answers
- It has a context window - a limit on how much text it can hold in one conversation
Understanding these properties is what separates someone who gets frustrated with AI from someone who knows how to get consistent, high-quality output.
2. LLM Comparison: Claude vs ChatGPT vs Gemini
| Dimension | Claude (Anthropic) | ChatGPT (OpenAI) | Gemini (Google) |
|---|---|---|---|
| Best known for | Long-form reasoning, nuanced writing, careful instruction-following | Breadth of capability, largest ecosystem, most integrations | Google Workspace integration, multimodal, search-connected |
| Strengths for PMs | Deep analysis, research synthesis, long documents, structured outputs | Versatility, code gen, image understanding, GPT Store, brainstorming | Real-time web access, native Google Docs/Sheets, data-heavy tasks |
| Weaknesses | No native real-time web in base mode, smaller plugin ecosystem | Can be verbose, over-hedges, cost at scale | Less nuanced on complex reasoning, enterprise privacy concerns |
| Context window | Very large (hundreds of thousands of tokens) - whole documents and codebases | Large, and larger still on the reasoning tiers | The largest of the three - up to ~1M tokens on the Pro tier |
| Best PM use case | Research synthesis, long briefs, structured analysis, automation prompts | Brainstorming, code, quick tasks, image analysis | Google Workspace workflows, real-time research, Sheets analysis |
| Pricing | Claude Pro $20/mo or API | ChatGPT Plus $20/mo or API | Gemini Advanced $20/mo or API |
Recommendation: For most growth PM workflows - research synthesis, experiment briefs, automation prompts, structured analysis - Claude is the strongest default. For brainstorming and quick tasks, ChatGPT is excellent. For anything living inside Google Workspace, Gemini wins on integration. Most power users have 2–3 and route by strength.
Think in tiers, not version numbers
Model names change every few months; the shape of the choice doesn't. Every major provider ships roughly three tiers, and picking the tier is 90% of the decision:
| Tier | What it's for | Rule of thumb |
|---|---|---|
| Frontier / reasoning | Hard analysis, long documents, multi-step planning, agentic work | Slowest and priciest - use when being wrong is expensive |
| Balanced / default | Everyday drafting, synthesis, summarising, most PM work | Your default in the chat UI |
| Fast / cheap | Tagging, extraction, classification, high-volume automation | Use inside loops and pipelines where you run it 1,000 times |
When you read a benchmark or a launch post, translate it into a tier before you change anything in your workflow. If your prompt only works on one specific version, the prompt is fragile - not the model.
3. Projects vs Skills: What's the difference?
A Project (Claude Projects, ChatGPT Custom GPTs) is a persistent context container. It lets you give the AI a standing system prompt ("You are always working as a growth PM at your company…"), upload reference docs it can always access, and maintain a consistent role and tone across sessions. Think of a Project as a pre-briefed AI colleague dedicated to a specific area of your work.
Examples for a Growth PM: "Growth Workspace" (role, ICP, OKRs, experiment log) · "User Research" (framework, taxonomy) · "Content & Copy" (brand voice, tone examples).
A Skill means different things in different tools - in agent frameworks it's a reusable instruction set, in Microsoft Copilot it's a plugin capability, in general usage it's a saved prompt template. The mental model: Projects are about persistent context. Skills are about reusable actions.
| Project | Skill | |
|---|---|---|
| What it is | Persistent context container | Reusable action / prompt template |
| Sets up | Who AI is, what it knows, how it should behave | What AI should do when triggered |
| Analogy | Onboarding a colleague with full context | Giving that colleague a standard operating procedure |
| PM use case | "Always know I'm a growth PM at my company" | "When I say 'write a debrief', follow this structure" |
4. Why MCP instead of a direct connection?
Direct API: you call an external service's API directly in your code or n8n flow. Works for simple linear workflows, full control over the call - but every new AI tool needs custom integration code, and there's no standardised way for the AI to discover available tools.
MCP (Model Context Protocol): a standardised protocol (open-sourced by Anthropic, widely adopted) that sits between the AI and your tools. Write the integration once as an MCP server; any MCP-compatible AI client (Claude, Cursor, Windsurf, etc.) can use it. The AI dynamically discovers tools, tools self-describe, and switching models doesn't break integrations.
The key insight: Direct API = you decide when to call the tool. MCP = the AI decides when to call the tool. MCP shifts integration from "developer wiring things together manually" to "AI dynamically using tools as needed." That's the difference between an AI that answers questions and an AI that takes actions in your systems.
| Situation | Use |
|---|---|
| One-off automation, linear flow, existing n8n node | Direct connection / n8n |
| Want any AI to always be able to query your internal data | MCP |
| Building something other tools or teammates will use | MCP (standardised) |
| Prototyping quickly, complexity not justified | Direct API |
| Want AI to autonomously decide when to pull data | MCP |
Prompts for Understanding LLMs & Choosing Your AI Stack
Which AI tool should I use for this?
I need to complete the following task: [DESCRIBE TASK IN DETAIL]. My context: I am a Growth PM. My most-used tools are Claude, ChatGPT, and Gemini. Recommend which AI tool is best suited for this specific task and why. Consider: reasoning quality, context window needs, integration requirements, real-time data needs, and output format. Be specific about which tier to use (frontier reasoning tier vs fast/cheap tier) rather than a version number, since versions change every few months.
Set up my AI Project
Help me set up a Claude Project (or ChatGPT Custom GPT) for the following use case: [DESCRIBE USE CASE - e.g. "growth PM work at your company"]. Write the system prompt I should paste into the project. It should include: 1. My role and context 2. The tone and format I want responses in 3. What I'll primarily use this project for 4. Standing instructions that should always apply 5. What the AI should always avoid Keep the system prompt under 500 words and make it immediately usable.
Should I build an MCP or use a direct connection?
I want to connect AI to the following tool/system: [DESCRIBE TOOL - e.g. "our experiment tracker in Notion", "our Contentful CMS", "our internal analytics API"]. My use case is: [DESCRIBE USE CASE]. Help me decide: should I build an MCP server, use a direct API connection, or use an existing integration (e.g. n8n node)? Consider: frequency of use, whether multiple AI tools need access, complexity of the integration, and whether the AI needs to autonomously decide when to query it. Give a clear recommendation with reasoning.
Exercise for Understanding LLMs & Choosing Your AI Stack
🛠 Exercise 1 - Run the same prompt across 3 models. Pick a real task from your week (e.g. "write a hypothesis for why our activation rate dropped"). Run the exact same prompt in Claude, ChatGPT, and Gemini. Compare output quality, structure, tone, and what each missed. Note which one you'd use for this task type going forward.
🎯 Exercise 2 - Set up your first AI Project. Create a Claude Project (or ChatGPT Custom GPT) for your growth PM work. Use the "Set up my AI Project" prompt to generate the system prompt. Test it with 3 real tasks. Notice the difference vs a blank conversation.
💡 Exercise 3 - Map your tools to the right model. List your 5 most common AI use cases. For each one, decide: Claude, ChatGPT, or Gemini - and why. This is your personal routing guide. Revisit it in 4 weeks and see if your answers have changed.
Resources for Understanding LLMs & Choosing Your AI Stack
Questions about this module
Which AI model should I use as a product manager?
Use Claude for long documents and careful writing, ChatGPT for general reasoning and image or data tasks, and Gemini when you need Google Workspace context. Most people keep two and switch by task.
What is a context window and why does it matter?
The context window is how much text a model can hold in one conversation. When you exceed it, earlier details are dropped, which is the most common cause of an assistant forgetting your instructions.
Do I need technical skills to start this course?
No. This module explains models in plain English, and every later module gives you copyable prompts and step-by-step exercises instead of code.
Related modules
Terms used here
All prompts from this module are also on the prompt library.