101 Terms Every AI Product Manager Should Master.
Are You Speaking the Code?
In the age of AI, Product Management is a competitive weapon. If your vocabulary stops at “agile” and “user story,” you are already obsolete. The true differentiator is mastering the language of inference, drift, and RAG. This list isn’t just a glossary; it’s the mandate for your professional survival.
🛠️ The PM’s Deep Toolkit: Terms to Master (Sample Full View)
The terms are grouped by their respective categories to provide maximum context.
Section I: Architectures & Cost
Term: RAG (Retrieval-Augmented Generation)
Definition: An architecture where an LLM fetches data from an external, proprietary knowledge base to inform its answer.
Ace the Interview: Frame this as the superior method for building factual, non-hallucinatory chatbots using private data.
Workplace Reality: The primary engineering task is managing the ingestion pipeline and data synchronization for accuracy.
Term: Inference Cost
Definition: The computational and API cost incurred each time the model generates an output.
Ace the Interview: Justify any high-cost feature by demonstrating the ROI is higher than this operating expense.
Workplace Reality: The main factor limiting adoption; you must constantly optimize prompts and model choices to reduce this cost per user.
Term: Context Engineering
Definition: Curating and formatting the background data (context) fed to the model before the user’s prompt.
Ace the Interview: Shows you understand the limitations of the Context Window and the need for data relevance and cost efficiency.
Workplace Reality: Optimizing the data input to improve model accuracy while aggressively minimizing Token count (the billing unit).
Term: MoE (Mixture of Experts)
Definition: An LLM architecture that routes queries to specialized sub-models, making massive models more computationally efficient.
Ace the Interview: Advanced architectural knowledge; use when discussing scaling or the latest model releases.
Workplace Reality: Determining if using an MoE model reduces Latency and Inference Cost enough to justify the architectural complexity.
Term: Tree-of-Thought (ToT)
Definition: Advanced prompting that explores multiple potential reasoning paths before selecting the most likely answer.
Ace the Interview: Use when asked about complex, high-stakes AI decision-making (e.g., medical diagnostics or strategic planning).
Workplace Reality: Implementing this technique for high-quality, complex problem-solving features, knowing it comes with increased Inference Cost.
Continued…
Section II: AI Safety, Risk & Metrics
Term: Evals (Evaluations)
Definition: Systematic, automated or human testing of model outputs against a set of “Gold Standard” answers.
Ace the Interview: The definitive answer to “How do you test AI quality?” Showcases operational rigor.
Workplace Reality: Designing and maintaining the “Gold Standard” dataset; managing the budget for human evaluators.
Term: Hallucination
Definition: When a model confidently generates false, fabricated, or nonsensical information.
Ace the Interview: The biggest risk; discuss mitigation strategies like RAG and setting safe Entropy.
Workplace Reality: Constant monitoring; prioritizing fixes for hallucinations that cause regulatory or reputational damage.
Term: Drift Detection
Definition: Automated recognition that the model’s performance has degraded due to changes in input data.
Ace the Interview: How you manage the unavoidable reality that all models eventually degrade over time.
Workplace Reality: Triggering an automated alert or initiating a retraining process based on detected decay.
Term: Jailbreak
Definition: A clever input designed to bypass a model’s safety filters and elicit restricted content.
Ace the Interview: Shows awareness of security risks and ethical Red Teaming.
Workplace Reality: Constant monitoring of user prompts and patching security layers to prevent harmful outputs.
Term: Red Teaming
Definition: The practice of hiring people or using tools to actively find and exploit model weaknesses before launch.
Ace the Interview: Essential safety practice; discuss as part of pre-release QA.
Workplace Reality: Scheduling and budgeting for continuous adversarial testing throughout the product lifecycle.
Term: Ethical Debt
Definition: The future cost and risk incurred by making unethical or socially irresponsible design choices today.
Ace the Interview: A great metaphor; shows you consider long-term, non-monetary risk.
Workplace Reality: Prioritizing a feature fix that addresses a minor Bias issue now to avoid major future reputational damage.
Continued…
📝 The “Cheat Sheet” for the rest of the 90 terms...
Grouped for quick scanning!
⚙️ Foundational ML, Deployment & Reliability
Latency (Time to wait for model response)
MLOps
Non-200 API Response
…
📈 Product Strategy & Business Outcomes
OKR (Objectives and Key Results)
ROI (Return on Investment)
TCO (Total Cost of Ownership)
…
🎨 Interaction Design & Workflow
Prompt Engineering
Few-shot Learning
Semantic Search
…
(...List continues in the full guide)
🔒 Your Next Career Move: The Full 101-Term Mastery Guide
If you are in an interview, don’t just drop these terms - contextualize them. Don’t say “I know what RAG is.” Say, “I would recommend a RAG architecture here because we need high factual accuracy and low latency.”
If you are on the job, remember: Users don’t care about ‘Entropy’ or ‘Evals.’ They care about solving their problems. Use these terms to build a better machine, but always sell the solution, not the tech.
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The remaining 81 terms, covering crucial sections, have been included in the full downloadable PDF: