When you add AI features to an existing product, the hardest part is often not the technology. It is the vocabulary. Engineers use terms that are technically correct, but the product implications are not always obvious.
You do not need to understand the math behind AI.
You do need to understand how these phrases affect scope, risk, cost, and user experience, and what decisions they trigger on the product side.
Think of this as a translation layer for product conversations.
| Engineer Says | What It Means for PM Decisions |
|---|---|
| “We need to choose a model” | Ask about cost, performance, and quality differences. This affects UX consistency, response speed, and operating expenses. Model choice is a product decision, not only technical. |
| “Inference cost might be high” | Consider pricing, usage limits, or feature gating. AI responses may have direct cost per use, which impacts monetization and scalability. |
| “We need guardrails” | Plan time for safety, compliance, and brand protection. This is not optional polish. It should be treated as part of the feature definition. |
| “The model might hallucinate” | Design UX for incorrect answers. Consider disclaimers, citations, confidence indicators, or human review flows to maintain trust. |
| “We can fine-tune it later” | Treat this as a future project with budget and timeline, not a quick tweak. Decide whether better accuracy is needed for launch or can wait. |
| “Latency might be an issue” | Expect slower responses than normal features. Plan loading states, streaming output, or asynchronous flows to manage user expectations. |
| “We can build an agent” | Clarify scope carefully. Agents move from answering questions to taking actions. This introduces permissions, audit trails, and higher failure risk. |
| “We need better prompts” | Allocate time for experimentation and iteration. Prompt design behaves like UX copy and product tuning, not one-time engineering work. |
| “We should use embeddings” | Expect additional setup to connect AI with company data. This improves relevance but adds technical complexity and maintenance. |
| “We need RAG” | Plan for knowledge base management and content quality. Retrieval improves accuracy but requires ongoing data ownership and governance. |
| “Token limits” | Be aware of input and output size constraints. This affects long documents, conversation memory, and feature design for summaries or uploads. |
| “Temperature” | Understand output variability. Lower values mean predictable responses. Higher values mean more creativity but less consistency. Choose based on feature goals. |
Why This Matters
Most product and engineering misalignment around AI does not come from disagreement. It comes from vocabulary gaps. When the same words imply different actions, scope drifts and expectations diverge.
You do not need to become an AI engineer.
You need enough translation ability to recognize when a technical phrase is actually a product decision in disguise.
