AI buzzwords decoded: A practical guide for product teams

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

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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