Technical AI Terms Product Managers Need to Know
You can't build AI products if you don't understand how AI actually works, and the gap between "traditional PM" and "AI PM" is closing fast.
Product cycles have accelerated, and teams are expected to move without a clear playbook. The pressure is on to act like an AI PM, even though most are still figuring out what that means.
Some product managers are building new AI features. Others are using AI to streamline their PDLC, from discovery to delivery. Either way, understanding how these systems work is now part of the job.
This glossary scratches the technical surface so you know where to dive deeper. Think of it as your starting point: the vocabulary that helps you ask better questions, spot feasibility issues earlier, and figure out what you actually need to learn next.
Modern AI PM Skills
Core topics, skills, and trends that PMs need to understand to build AI-native products.
Prompt Engineering
The art of crafting inputs that reliably produce the outputs you want.
Prompt engineering is the practice of writing clear instructions that guide how a language model responds. It involves specifying the task, desired format, and any constraints so the model understands what you need. Good prompts are specific ("List three actionable insights" vs. "Tell me what you think"), provide context when needed, and iterate based on what the model produces.
This skill matters in two ways for PMs. First, it's essential for getting better outputs from AI tools you already use—whether that's ChatGPT for research synthesis or an internal assistant for drafting specs. Second, when building AI features, prompt quality often determines whether a feature feels usable on day one. Well-written prompts reduce ambiguity and hallucinations, making output more consistent across users. They also surface limitations early, which helps teams decide what needs deeper system support later. While less scalable than architectural approaches like context engineering (more on that later), prompt engineering is the fastest way to test ideas and identify where systems break down.
Product Management Example: You're analyzing a set of customer interviews using ChatGPT to identify common pain points. The initial summary misses key action items and feels too generic. You revise your prompt to ask for a bulleted list of action items phrased as imperative verbs (e.g., "Improve onboarding flow," "Clarify pricing"). The output becomes sharper and easier to scan, so you save the prompt and sample output to reuse in future discovery work.
Temperature Tuning
Adjusting model randomness to control output consistency and creativity.
Temperature is a setting that controls how much randomness a model introduces when selecting its next word or phrase. It typically ranges from 0 to 1 (or sometimes higher). At temperature 0, the model always picks the most probable next word, producing consistent, predictable outputs. As you increase temperature, the model considers less likely options, introducing more variation in tone, structure, and word choice—which can make responses feel more human or creative.
PMs use temperature tuning to shape the UX of AI features. Small adjustments can impact consistency, brand alignment, and user trust. Choosing the right value is less about technical optimization and more about interpreting feedback, understanding context, and matching model behavior to user expectations. It's a balancing act between flexibility and control.
Product Management Example: You're building an AI assistant that helps sales reps write outbound emails. At a temperature of 0.2, the messages sound safe but repeat similar phrasing across accounts. At 0.8, the language feels more human but sometimes drifts off-brand. After testing engagement and review feedback, you ship at 0.6 to achieve a tone that feels flexible without losing control.
Fine-Tuning
Train a model on custom data to improve performance for specific domains or tasks.
Fine-tuning takes a general-purpose model and specializes it using your own dataset. Instead of relying solely on prompts or retrieved context, you're updating the model's internal behavior to align with your product's needs. This approach makes sense when you have consistent patterns that prompts can't reliably capture, or when you need the model to adopt specific terminology, tone, or reasoning styles that aren't well-represented in its base training.
For PMs, fine-tuning is a strategic decision with real tradeoffs. It requires quality training data, engineering investment, and ongoing maintenance as your product evolves. But when it works, it delivers more consistent outputs and can reduce reliance on complex prompting or retrieval systems. The key is knowing when the investment pays off—typically in high-volume, high-stakes workflows where quality predictability justifies the cost.
Product Management Example: Your AI feature generates technical documentation for enterprise software. General models struggle with your company's internal terminology and documentation standards, producing outputs that require heavy editing. After collecting 500 manually reviewed examples, you fine-tune a model specifically for your docs. The fine-tuned version understands your style guide and technical vocabulary, cutting review time by 60% and making the feature viable for your technical writing team.
Unit Economics
A framework for deciding if an AI feature is worth the cost per query.
Unit economics describes the cost benefit analysis of running an AI feature at the level of a single interaction. For product managers, this means understanding the cost of each model call and how that spend maps to user impact and business outcomes. The value of AI-powered products must be measured against real usage patterns, latency expectations, and margins that scale as adoption grows.
This framework becomes especially important as teams move beyond experimentation into production. High-quality models often deliver stronger reasoning and more polished outputs, but they also introduce higher per query costs that compound quickly. PMs use unit economics to decide where premium intelligence is justified and where simpler approaches are sufficient. The goal is not to minimize cost at all times, but to align spend with impact in a way that supports sustainable growth.
Product Management Example: You're evaluating an AI feature that generates detailed competitive analyses for sales teams. Early tests show that top-tier models produce the most accurate insights, but each request carries a meaningful cost. By analyzing usage data, you learn that only a small subset of users needs that depth on every query. So, you route routine requests to lower-cost models and reserve premium models for high-value workflows.
Human-in-the-Loop
Ensuring human oversight where AI decisions carry real consequences.
Human-in-the-loop refers to systems where people review and potentially correct outputs before they are finalized. This skill is especially critical when decisions carry real consequences for users. In regulated or high-risk domains, particularly those dealing with personally identifiable information (PII), oversight ensures that automation is applied with appropriate judgment and accountability.
For product managers, this means designing workflows that intentionally include human review at critical points. The goal is to place people where their judgment meaningfully affects outcomes. Reviewer input can then be captured and reused to improve system performance over time.
Product Management Example: You ship a feature that flags potentially fraudulent user accounts. The system surfaces high risk cases to a trust and safety team and provides a clear explanation for each flag. Reviewers decide on the appropriate action, and their decisions are logged for future evaluations. This improves accuracy over time and keeps human judgment involved where risk is highest.
When not to use human-in-the-loop: Not every AI workflow needs human oversight. Low-stakes, high-volume tasks—like categorizing support tickets, generating meeting summaries, or suggesting email subject lines—can run autonomously if guardrails and evaluations demonstrate consistent quality. The decision depends on consequence severity and cost of manual review. PMs balance risk reduction against operational efficiency, reserving human oversight for decisions where errors carry meaningful impact.
Full Stack Builder
PMs who design, code, and ship products end-to-end, leveraging AI to work across the full stack.
A full stack builder is a product manager who goes beyond writing requirements and starts interacting directly with AI systems. Instead of handing off ideas as abstract specs, they test assumptions by working hands-on with large language models. That might mean experimenting with prompts, calling an API in a lightweight prototype, or adjusting context inputs to see how output quality changes. They aren't engineers, but they understand enough of the stack to explore ideas independently and spot feasibility issues early.
This shift reflects how the PM role is changing in AI-powered teams. When behavior is probabilistic, clarity often comes from experimentation rather than documentation. Full stack builders shorten feedback loops by validating concepts in hours instead of weeks. They can sense when an idea is limited by data, model choice, or system design, and they bring those insights into planning conversations with credibility.
Product Management Example: You want to explore an AI-powered onboarding assistant. Thankfully, you don't have to wait for engineering capacity to test the idea. You prototype a simple flow using an LLM API and experiment with prompt structure. Then, you observe where users get confused or where responses break down. Those learnings shape the roadmap and influence model selection, reducing rework down the line.
Systems Design for LLM-Powered Products
How to architect AI features that are scalable, reliable, and useful.
Context Engineering
Design the knowledge inputs AI needs at inference time.
Context engineering is the systematic design of the information environment a model relies on when generating a response. Instead of focusing on how instructions are written (prompt engineering), this practice centers on how relevant knowledge is selected and delivered at the moment a request is made.
For PMs using AI tools, this means manually adding context—pasting documents, data, or background information into prompts to improve outputs. For PMs building AI products, context engineering becomes a system design decision: architecting how the product automatically retrieves and injects relevant context at runtime. This might include internal documents, operational data, or real-time business information. The quality of an output often depends less on clever instructions and more on whether the right source material is available in that moment.
Product Management Example: Rather than asking users to paste a persona or background details into a tool each time, you design the system to retrieve strategy documents and OKRs automatically when someone types "Draft a feature spec." The model receives the same structured inputs every time, which leads to more consistent results and less manual effort.
RAG (Retrieval Augmented Generation)
A system design pattern that retrieves external data to ground LLM responses in verifiable sources.
RAG is a system design pattern that allows a model to retrieve information from a private knowledge base at the time a question is asked. Instead of relying on what the model already knows, the system searches approved sources and passes the most relevant material into the prompt before generating a response. This approach treats internal data as the source of truth and keeps outputs tied to real evidence.
RAG changes how AI features deliver insight. It makes answers more specific, traceable, and aligned with the business context. By grounding responses in internal documents (e.g., tickets, sales decks, etc.), teams reduce guesswork and increase confidence in the results users see.
Product Management Example: To better prioritize the roadmap, you ask your company's internal RAG chatbot: "What reasons did enterprise customers give for canceling?" Without retrieval, the chatbot produces broad explanations based on general patterns. With RAG, it searches the most recent Zendesk tickets and identifies the 15 conversations from enterprise customers that mention cancellation. You now have a summary of the actual reasons customers gave. When each insight can be traced back to a real source, decisions are easier to defend.
Embeddings
Convert text into numbers so AI systems can measure similarity and relevance.
Embeddings transform text into mathematical representations called vectors. These vectors capture semantic meaning, allowing systems to find related content even when exact words don't match. When a user asks "How do I cancel my subscription?", embeddings help the system understand that's similar to "terminating my account" or "ending my membership"—without relying on keyword matching.
For PMs, embeddings are the engine behind RAG and semantic search. They determine which documents get retrieved, how relevant results feel to users, and whether the system can connect ideas across different phrasing. Quality embeddings improve answer accuracy without changing the model itself. Understanding embeddings helps PMs diagnose why retrieval feels off or why certain queries return irrelevant results.
Product Management Example: Your internal knowledge base search returns exact keyword matches but misses conceptually related documents. A PM searches for "pricing strategy" but doesn't find the relevant "monetization approach" document. After implementing embeddings-based search, queries start returning semantically similar content even when terminology varies. Search satisfaction improves because the system now understands meaning, not just matching words.
MCP (Model Context Protocol)
A universal integration standard for AI-based systems.
MCP is a standard that allows an AI application to connect to external tools through a shared interface. Instead of building custom integrations for every service, the system relies on a common protocol to exchange context and actions. This approach reduces engineering overhead and makes integrations easier to maintain as the product grows.
Model context protocol changes how integration work is scoped and prioritized. Rather than planning one-off connectors, teams can invest in a reusable foundation that supports many tools. This shifts roadmap discussions from individual integrations to long-term platform capability. MCPs enable teams to move from building integrations to supporting an ecosystem.
Product Management Example: Your product needs to integrate with Zendesk, Salesforce, and Notion. Without a shared protocol, each integration requires custom logic, separate testing, and its own engineering effort. Adding Linear later means repeating all that work. With MCP, you build support for the protocol once. After that, connecting to any new tool—Zendesk, Salesforce, Notion, Linear—becomes a configuration task, not a custom build. When a customer asks if your product works with their tools, the answer is yes because the system speaks a common language.
Model Routing
Smart delegation of tasks to the right model based on cost, speed, or quality.
Also known as model orchestration, model routing refers to the logic that determines which model handles a given request. The decision is based on factors such as task complexity and expected response time. By routing requests dynamically, teams avoid relying on a single model for every interaction and can align behavior with product requirements.
This delegation shapes the user experience while keeping spend predictable. Different users and workflows place different demands on the system. Routing allows PMs to match model capability to user needs without overprovisioning intelligence where it is not required.
Product Management Example: You're building a feature that generates competitive summaries. Requests from free tier users are sent to a fast, lower cost model that delivers concise overviews. Requests from enterprise users are routed to a more advanced model that produces deeper analysis. You log latency, cost-per-query, and feedback to refine your routing logic over time. This approach shows how model routing supports scalable AI features in production.
Agent Graphs // Agentic Graphs
Orchestrate multiple specialized agents working together in sequence or parallel, instead of asking one model to handle an entire complex task.
Agent graphs model workflows as a series of connected nodes. Each node is a specialized agent—one researches, another analyzes, another synthesizes—and edges define how information flows between them. This is the literal code structure that executes when your feature runs, not a planning diagram. The graph defines which agent runs when, what data it receives, and where its output goes next.
Breaking work into stages creates clear evaluation points where you can measure and improve specific agents without rebuilding everything. Product managers don't write PRDs in a single uninterrupted pass; they research, outline, draft, and revise. Agent graphs enable similarly structured workflows.
This approach is central to how Productboard Spark supports product teams. Spark uses coordinated agents to help PMs do real product work—exploring product ideas, analyzing customer needs, and generating briefs. Instead of dumping everything into one massive prompt, Spark progresses through distinct stages: a research agent gathers relevant context, an analysis agent identifies patterns, and a generation agent drafts documents. Each stage produces artifacts that feed into the next, creating an audit trail and enabling targeted improvements.
Product Management Example: You're building an AI feature that creates competitive analysis reports. Your first version uses a single prompt that asks the model to research competitors, compare features, and write a summary—all at once. Results are inconsistent; sometimes it skips competitors, other times it invents features. You redesign using an agent graph: Agent 1 searches for and validates competitor information, Agent 2 extracts feature lists from each competitor's site, Agent 3 structures the comparison, and Agent 4 writes the narrative summary. Now you can evaluate each step independently, and when reports have issues, you know exactly which agent to improve. Quality becomes predictable because complexity is distributed across manageable stages.
Cost & Constraints
The technical boundaries that shape what's possible and profitable with AI.
Tokens
The atomic unit of cost, speed, and capacity in LLM systems.
Tokens are the chunks of text that models read and generate. Roughly speaking, one token equals about four characters or three-quarters of a word. When you send a request to an LLM via an API (an application programming interface that allows your product to communicate with the model), you're charged based on tokens processed—both the input you send and the output the model generates.
Token limits exist because processing text requires computational resources—specifically memory and GPU capacity. Each token the model handles consumes a portion of this capacity. Models set maximum context windows (typically ranging from 8,000 to 200,000+ tokens) to ensure reliable performance and manage infrastructure costs. When requests exceed these limits, they either fail or get truncated.
Understanding tokens helps PMs estimate costs, predict latency, and diagnose why certain requests fail or feel slow. Token awareness shapes product decisions: it determines how much context you can include in a request, how long responses take to generate, and whether your unit economics work at scale. When a feature costs more than expected or takes too long to respond, tokens are usually the first place to investigate.
Product Management Example: Your AI feature summarizes customer feedback threads. Early testing shows that 20% of requests fail silently. After investigating, you discover these threads exceed the model's 8,000-token context window. You implement chunking logic that splits long threads into manageable segments, processes them separately, and merges the summaries. Token awareness turned a confusing failure pattern into a solvable design problem.
Context Window
The working memory limit that determines how much information a model can process at once.
Context window refers to the maximum amount of text a model can "see" in a single request—including your prompt, retrieved documents, conversation history, and any instructions. Once you hit this limit, the model either truncates information or fails entirely. Different models offer different window sizes, and larger windows typically cost more per token.
For PMs, context window is a hard constraint that shapes feature scope. It affects how many documents you can include in RAG retrieval, how long a conversation can run before losing thread, and whether you can fit an entire codebase or specification into a single analysis. Managing this constraint often requires creative system design—chunking content, prioritizing what gets included, or switching to models with larger windows for specific use cases.
Product Management Example: You're building a feature that analyzes quarterly business reviews (QBRs) and generates executive summaries. QBR documents average 15,000 tokens, but your model's context window tops out at 8,000. Rather than switching to a more expensive model with a larger window, you design the system to extract key sections (goals, metrics, risks) and process only those. The summaries lose some nuance but cover 90% of what executives need, and the feature ships within budget.
Latency
How fast a model responds—and how that speed shapes product experience.
Latency refers to the time it takes for an AI system to generate and return a response after receiving a request. In AI-powered products, latency directly affects how responsive a feature feels. Even a few seconds of delay can make a tool feel sluggish and interrupt user flow, reducing trust in the system.
Understanding and managing latency influences model selection, UX design, and even routing logic. Optimizing for latency often involves tradeoffs. Faster models may be cheaper or feel snappier, but slower models may provide more accurate or nuanced output. The goal is to align system performance with user expectations for each specific use case.
Product Management Example: You're launching an AI feature that generates instant reply suggestions for customer support agents. Early tests with a high-quality model return great responses, but each request takes 10–15 seconds (too slow for real-time use). You test a smaller, faster model that returns responses in under 2 seconds, with slightly reduced quality. After piloting both options, you choose the faster model and supplement it with post-editing tools so your CX team can quickly personalize replies without starting from scratch.
Evaluation, Accuracy & Performance
How to make sure your AI is actually working.
Evaluations
Unit testing for LLM outputs, grounded in gold datasets.
AI evaluations are structured tests that measure how well a model performs against a defined set of expectations. Since model outputs can vary from run to run, quality can’t be assessed through spot checks alone. Evaluations use a fixed set of questions paired with known "good" answers so performance can be measured consistently over time. They provide a shared way to judge whether an AI feature is meeting its goals.
They make it possible to track changes in quality as prompts, models, or even context sources evolve. Evaluations create a baseline that supports informed decisions during iteration and release planning.
Product Management Example: As the owner of an AI feature that generates first drafts of internal policy documents, you assemble a gold dataset of past policies that have already passed legal review. Each update to prompts or source material is tested against this dataset before release. When evaluations show missing clauses or incorrect language, you revise the system before shipping. You've successfully created a clear quality bar for a high-risk workflow.
Guardrails
Rules that constrain AI behavior to reduce risk and protect trust.
Guardrails limit what a model is allowed to say or do within a product. They define boundaries that keep outputs aligned with policy and intended use. These controls are especially important once AI features reach production, where mistakes can affect users and the business.
For PMs, guardrails are a design responsibility rather than a technical afterthought. They shape how the system behaves in uncertain situations and determine when it should defer or decline to answer. Clear rules make behavior predictable and easier to explain to stakeholders. They can be hardcoded rules (e.g., never give medical advice), filters (e.g., block outputs with PII), or logic layers (e.g., fallback to static answers if confidence is low).
Product Management Example: Your leasing agency launched a chatbot that helps renters understand lease terms. To avoid providing hallucinated legal advice, the system checks whether an answer can be directly supported by the uploaded lease document. If it cannot, the chatbot responds with a prompt to contact the leasing office.
Hallucinations
When AI generates confident-sounding but factually incorrect or fabricated information.
Hallucinations occur when a model produces outputs that seem plausible but aren't grounded in reality or the provided context. The model might cite sources that don't exist, invent statistics, or confidently assert false information. This happens because language models are trained to predict probable next words, not to verify truth. When they lack information or context, they'll sometimes fill gaps with convincing-sounding fabrications rather than admitting uncertainty.
For PMs, hallucinations are a trust and liability risk. They're especially dangerous in domains where users expect factual accuracy—customer support, legal guidance, financial analysis, or medical information. Reducing hallucinations requires layered approaches: grounding outputs in retrieved data (RAG), implementing guardrails that flag uncertain responses, designing UX that encourages verification, and using evaluations to catch fabrications before they reach users.
Product Management Example: Your AI feature helps sales teams research prospects by summarizing company information. Early users report that the tool sometimes invents executive names, funding rounds, or partnerships that don't exist. You implement several mitigations: require all facts to cite specific sources, add a confidence score to each claim, design the UI to prominently display sources alongside summaries, and create evaluations that test for factual accuracy against verified data. Hallucination rates drop by 70%, and user trust increases because they can verify every claim.
Data Preprocessing
Clean, organize, and transform inputs to reduce model confusion.
The critical work done on inputs before they reach a language model is referred to as data preprocessing. Raw data is often inconsistent and unstructured, which makes it harder for the model to find meaning. Preprocessing turns that input into structured signals that are easier to analyze and summarize.
Preprocessing decisions directly affect output quality for product teams. When inputs are messy, outputs are unpredictable; even strong models produce unreliable results. By cleaning and transforming data at the source, teams reduce noise and improve the accuracy of every analysis built on top of that input. Investing in preprocessing often delivers bigger gains than changing prompts or switching models.
This work pairs naturally with tools like Productboard Pulse, which collects and synthesizes customer input from every source to reveal trends and actionable signals. Pulse makes it possible to monitor feedback while automatically exploring key topics and surfacing insights that inform strategy and planning. Thoughtful data preprocessing ensures that this kind of voice of customer analysis starts from clean, high-quality inputs rather than noise.
Product Management Example: You're feeding thousands of incident reports into an LLM. The model gets confused by emojis and automated system alerts mixed into real user content. In response, you undergo data preprocessing to remove system-generated text. After grouping messages by user and filtering out very short messages that add little context, response accuracy improves by 30%.
Synthetic Data Generation
Privacy-safe test data to simulate edge cases or regulated domains.
Synthetic data generation creates realistic but fabricated data for testing and evaluation. The data mirrors the structure and patterns of real information without exposing sensitive records. This allows teams to validate system behavior while staying compliant with privacy and regulatory requirements.
Synthetic data unlocks testing in areas where real data access is limited or restricted. It supports broader coverage of scenarios for PMs and helps teams explore edge cases that may be rare in production. Well-designed synthetic datasets make it possible to evaluate features earlier in the development cycle.
Product Management Example: Working on a HealthTech product that must comply with HIPAA, you need to test whether an AI feature can summarize patient history. Developers, of course, cannot access real patient records. So, you generate a large set of fake medical records that include realistic conditions and lab results. This dataset is used to stress test the summarization feature and identify gaps before release.
AI is now embedded in how product work gets done
The shift is already underway. AI isn't showing up as a standalone feature teams bolt onto existing products—it's becoming part of the workflow itself. Product managers are now navigating questions about token costs, context windows, and hallucination risks alongside traditional tradeoffs around scope and timeline. Understanding how models receive context, how outputs are evaluated, and where humans should remain in the loop is no longer optional expertise.
The concepts in this glossary represent the common language emerging across AI-powered product teams. When PMs can speak clearly about embeddings, agent graphs, guardrails, etc., they move faster. They make better decisions about when to automate, when to involve humans, and how to scale responsibly. The work still demands judgment, but that judgment is now informed by a practical understanding of how these systems actually behave.
The strongest teams treat these foundations as a shared toolkit. Whether refining an internal process or building for end users, the goal remains the same: clarity, leverage, and consistent quality. When product managers understand the systems they're building with, AI stops being a black box and becomes a reliable tool in the product craft.
To see how these principles show up in real product workflows, watch the Building AI Products That Actually Work webinar on demand.
