You don’t need to be an AI engineer to lead in an AI-powered world. This guide breaks down the most common AI buzzwords — like MCPs, tokens, and agents — so product teams can stop guessing and start speaking the same language as their technical partners.
- AI tools are helping teams move faster, but most people still treat them like a black box.
- Understanding what powers those tools helps you make better decisions, build smarter features, and work more effectively across functions.
- You don’t need to become technical — but you do need to get fluent in the terms.
MCPs (Model Context Protocols): How AI understands its role
- Think of MCPs as structured context — instructions that help the model behave consistently
- ✅ Relevant to writers, engineers, designers, and PMs: helps define AI roles clearly (e.g., tone, scope, task limits)
- Good context improves accuracy and makes tools feel less “off”
- MCPs allow you to connect AI’s, engineers can use an AI powered tool and fetch a model from a different AI using MCP to enhance it’s knowledge
Source prompts: The hidden script behind every AI tool
- Most AI tools use a “source prompt” to guide behavior before you type anything
- ✅ Anyone using AI tools should know how this shapes tone, format, and boundaries
- Editing this is often the simplest way to improve performance
Agents: Not just chatbots — they take actions
- Agents can follow steps, trigger actions, or call APIs (e.g., booking, summarizing, emailing)
- ✅ Important for anyone designing workflows or thinking about automation
- Knowing whether a tool is using an agent helps you spot limitations and edge cases
RAG (Retrieval-Augmented Generation): Where AI pulls its facts from
- RAG allows models to pull information from outside sources (e.g., your docs, product data)
- ✅ Helps clarify how tools like AI chatbots or search features stay “informed”
- Poor retrieval = hallucinations, broken UX
Tokens: The hidden cost and speed tradeoff
- Tokens are the currency of AI — every word you type or generate has a cost
- ✅ Designers and PMs should know this affects latency, cost, and experience
- Better token management = faster, cheaper, clearer tools
Prompting vs. fine-tuning: Which one actually matters
- Prompting = instructions; Fine-tuning = retraining the model
- ✅ Most product use cases don’t need fine-tuning — just better prompt design
- Helps product teams avoid overcomplicating solutions
Embeddings: How AI remembers and connects ideas
- Embeddings turn text into math — allowing AI to “remember” and find meaning
- ✅ Powers smart search, recommendations, and memory in tools
- Teams working on UX or data should know how this works at a high level
What should product teams explore next?
- Prompt libraries, model playgrounds, and AI workflow tools (like LangChain, Inngest, OpenAI Functions)
- ✅ Encourage curiosity: play with tools, read source prompts, talk to your engineers
- ✅ Push for transparency: What model is being used? What data does it rely on? What’s the fallback?
- A recommended free resource to learning more about AI: deeplearning.ai
Final thought:
- You don’t need to build the stack — but you do need to speak the language
- Understanding AI concepts helps product teams ask smarter questions, spot risks early, and co-create better experiences
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