What to ask before you get started: your first week on the job

A first-week question list for a new marketing hire, a founding marketer, or a fractional consultant: what to ask the founder, the product, sales, and the people who inherit your framework.

Product marketing
August 18, 2026
·
6 min
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Abstract brand form on a purple field

Your first deliverable in a new marketing job is a list of questions, not a deck. That's unpopular in week one, when the calendar already carries launch dates somebody committed to before you signed, and the fastest way to look useful is to produce something. It's also the fastest way to be wrong.

Gallup found that only 12% of employees strongly agree their organization does a great job onboarding new employees, and that turnover can run as high as 50% in the first 18 months! Most of us get no real handover. So the structure has to come from us, and it takes the shape of what we ask and who we ask it of.

Writing positioning before you understand the product is like drawing a map of a city you've only seen from the plane. The outline comes out roughly right, every street is wrong, and nobody finds out until somebody tries to use it to get somewhere. So what do you actually ask, in what order, and who owns the answers?

Why is week one the cheapest week you'll ever have?

Because understanding is the part of the work that everybody agrees with in principle and compresses in practice. It looks like preparation rather than output, so it's the first thing anyone trims when a launch date moves, and it's the one part where compressing it guarantees you pay later. The bill arrives around month six, when the messaging framework everybody signed off on turns out to describe a buyer who doesn't exist and never did.

In deep tech the bill is bigger. The buyer is often an engineer, an architect, or a data scientist, and they read your website the way they read a spec sheet. If the value claim is thin, they don't argue with it. They close the tab.

That's true whether you're the first marketing hire, a founding marketer, or a fractional consultant with a twelve-week scope. The fractional version is harder, because nobody is paying you to sit and read, so ask anyway, in the first days, while being new is still a license to ask anything.

Who do you ask, and what do you ask them?

Whoever decided there was a need

Usually a founder, an owner, or the VP of Product who wrote the job description, and a vague answer here is data in itself.

The product

You learn a product by using it, not by reading the deck somebody made about it. Ask for a license, a sandbox, and the same onboarding a customer gets, then run the workflow end to end and write down every place you got stuck. Those notes are the closest thing to a new buyer's experience anyone in the building will have for another year.

People already talking to buyers

Sales, customer success, and support already hold most of the answers that marketing spends its first month guessing at. Why five recorded calls and not one? Because one call is an anecdote, five is a pattern, and you can't hear a pattern in a summary somebody else wrote for you.

The people who inherit it

Product managers, engineers, technical writers, and designers all inherit your framework and any of them can ignore it without ever saying so, so ask them in week one and they own it with you. Read what is internal, too, not only what is published: the specs, the support tickets, the Slack thread where somebody explains the product properly to a colleague.

What does that look like on a calendar?

DayWhere the time goesWhat exists by the end1Kickoff with whoever opened the roleGoals and deadlines, in writing2The product, hands on, plus the internal docsStuck points and a glossary3Recorded buyer calls, sales, supportObjections, competitors, buyer words4Product, engineering, docs, designDifferentiators split from baseline5Writing it up, then reading it backA one-page brief and a list of unknowns

Day five is the one people skip, and it is the one that pays for the other four. Reading your understanding back to the people who gave it to you catches the misunderstanding while it is still free to fix, and it gets you the correction in front of witnesses.

What do you do with the answers?

They go into one document, not five slides. The positioning and messaging brief keeps the same spine every time, whether the team reading it is one founder or the 13 direct reports I had at Coro, and the questions above map onto it directly.

Does answering all of it guarantee that the positioning is right? Of course not. It guarantees the positioning is checkable, which is a smaller claim and a more useful one, because every line in it traces back to a person who said it and a call you can replay. Positioning built on a guess is positioning you rewrite in six months, and the same first-week sequence sits underneath the GTM framework work case.

Then act on what comes back, including the times when the answer is inconvenient. At Visual Layer, a computer vision company, we had a product-led growth motion underway, and after a product-market-fit analysis with the founders I cancelled it and moved us to account-based marketing and direct enterprise sales. ARR went from under $1M to $3M, and Camtek acquired the company in April 2026.

Years earlier at Amdocs, a manager told me he knew I could do the job and hadn't seen it yet. He was right. What he wanted was somebody who understood the value of the product before writing a word about it, and across 25 years in the industry that order of operations hasn't changed.

Key takeaways

If you are starting somewhere next month, or you have just hired someone who is, bring that first week to a free 30-minute call. We'll go through the list together, cut the questions your product does not need, and you'll leave with a version your team can hand to the next person who walks in.

Mentoring, free forever.

I mentor marketers and technical writers. Breaking in, growing your career, automating with AI and more.

Book a free session
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Data Augmentation
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workflow
Workflow
The ordered steps and hand-offs by which work moves from start to finish, who does each one and what triggers the next.
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verification
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Confirming that a result, label or component is correct by checking it against a reference or a second method.
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A query-engine technique that processes whole columns of values in one CPU instruction instead of row by row, which is what makes engines like DuckDB fast.
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Vector Space
The multi-dimensional space where embeddings live; distance within it stands for how similar two items are.
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The ability to follow any output back through the data, labels, code and model versions that produced it.
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Splitting text into the units a language model works with, which may be words, parts of words or characters.
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Token Limit
The maximum number of tokens a language model can take in and produce in one request, which caps how much context it can hold.
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Threading
Running several parts of a program at the same time on separate threads so work overlaps instead of waiting in line.
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Temporal Analysis
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Service Level Agreement: the committed level of service a provider promises, such as uptime, response time or support hours, with consequences if missed.
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Generating realistic scenarios in software to test or train a system where real data is scarce, risky or expensive to collect.
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Similarity Search (Vector Search)
Finding the items whose embeddings sit closest to a query's embedding, which returns things that look or mean alike rather than share keywords.
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The initial configuration of a project: connecting data sources, choosing models and settings, and defining the workflow before work begins.
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Dividing an image into regions by labelling every pixel, so the exact shape of each object or area is known.
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Search Attributes
The fields a search can filter or rank on, such as labels, tags, dates, camera id or model confidence.
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search-and-retrieval
Search & Retrieval
Finding the items in a collection that best match a query, whether by keyword, metadata or visual and semantic similarity.
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Scene Understanding
Interpreting a whole image or video as a scene: what objects are present, where they are and how they relate.
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Saliency Map
A heat map over an image showing which pixels most influenced a model's prediction, used to explain and debug it.
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Reward Function
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Review Batch
A set of items grouped for a human reviewer to check in one sitting, often chosen because the model was unsure about them.
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Relational Database
A database that stores data in tables with rows and columns and links them through keys, queried with SQL.
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Regularization
Techniques that stop a model memorising its training data, such as penalising large weights or dropping random neurons, so it generalises better.
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refactoring
Refactoring
Restructuring existing code to make it cleaner and easier to change without altering what it does.
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recipe
Recipe
A saved, repeatable set of processing steps or settings that can be rerun on new data to get the same kind of result.
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Retrieval-Augmented Generation: a language model that first fetches relevant documents and then writes its answer from them, which cuts hallucination.
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The checks and processes that make sure a product, dataset or model meets its standard before it ships.
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Predictive Maintenance
Using sensor data and models to forecast when equipment will fail, so it is serviced just before it does rather than on a fixed schedule or after a breakdown.
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Policy
In reinforcement learning, the strategy that maps each state to the action the agent should take.
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The smallest unit of a digital image, a single point with a colour or intensity value; images are grids of them.
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A PostgreSQL extension that stores embeddings and runs similarity search inside the database, so vector search needs no separate system.
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Learning to recognise a new class from a single example, usually by comparing against a learned notion of similarity.
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Storage that keeps files as objects with metadata in flat buckets rather than folders, built for huge volumes and cheap durability.
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Rescaling values to a common range or distribution so no single feature dominates and models train more stably.
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Random or irrelevant variation in data that hides the real signal, from sensor error, compression, lighting or sloppy labels.
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Multi-Version Concurrency Control: a database technique that keeps multiple versions of a row so readers and writers do not block each other.
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Continuously watching a deployed system's health and output quality so drift, errors and slowdowns are caught early.
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Model Versioning
Tracking each trained model with its data, code and settings, so any version can be reproduced, compared or rolled back.
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metrics
Metrics
The numbers used to judge a model or system: accuracy, precision, recall, F1, latency and others chosen to match the goal.
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metric-learning
Metric Learning
Training a model so that similar items end up close together in its embedding space and dissimilar ones far apart.
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Training a model to learn how to learn, so it adapts to a new task from very few examples.
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A pixel-level map marking exactly which pixels belong to an object or region, the output of segmentation.
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The mathematical frame for reinforcement learning: states, actions, rewards and transition probabilities where the next state depends only on the current one.
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The formula that scores how wrong a model's predictions are during training; training works to make this number as small as possible.
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One individual occurrence of an object in an image, as distinct from the class it belongs to.
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indexing
Indexing
Building a lookup structure over data so searches find matches without scanning everything.
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image-processing
Image Processing
Operations applied to pixels, such as resizing, filtering, thresholding or colour correction, usually to prepare images for analysis.
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Hyperparameter selection
Choosing the settings that control how a model learns, such as learning rate, batch size or network depth, which are set before training rather than learned.
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grid-search
Grid search
Trying every combination of chosen hyperparameter values in turn and keeping the set that scores best.
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gradient
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The direction and size of change in a model's error for each weight, used during training to decide how to adjust that weight.
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Graphics Processing Unit: a chip built for many parallel calculations at once, which makes it the workhorse for training and running neural networks.
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Generative AI
AI that creates new content such as text, images, code or audio from a prompt, learned from patterns in large training sets.
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generalization
Generalization
A model's ability to perform well on data it has never seen, rather than only on its training examples.
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feedback-loop
Feedback Loop
A cycle in which a system's outputs, or human reactions to them, are fed back as training signal so it improves over time.
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feature-map
Feature Map
The output of one layer in a convolutional network: a grid showing where in the image a particular pattern was detected.
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feature-engineering
Feature Engineering
Creating or transforming input variables so a model can learn from them more easily, using domain knowledge rather than raw data alone.
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fairness
Fairness
A model behaving without systematic disadvantage to groups defined by traits such as gender, age or region.
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exploration-vs-exploitation
Exploration vs. Exploitation
The trade-off in learning systems between trying new actions to discover better options and repeating actions already known to work.
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evaluation
Evaluation
Measuring how well a model performs against held-out data using agreed metrics, before and after it is deployed.
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efficiency
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domain-shift
Domain Shift
When the data a model meets in production differs from the data it was trained on, so accuracy drops even though nothing in the model changed.
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dml
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Data Manipulation Language: the SQL commands that read and change rows, such as SELECT, INSERT, UPDATE and DELETE.
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A self-supervised vision method from Meta that learns strong image features without labels by teaching a network to match different views of the same image.
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ddl
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Data Definition Language: the SQL commands that create and change the structure of a database, such as CREATE TABLE and ALTER.
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Database Reliability
A database's ability to stay available, correct and recoverable under load, failure and change.
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data-quality
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The record of where data came from and every transformation it went through, so any result can be traced back to its source.
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A store that keeps raw data in its original format at any scale, structured or not, for later processing and analysis.
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Data Integrity
Data that is complete, accurate and unaltered over its whole life, protected from corruption, loss or unauthorised change.
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data-ingestion
Data Ingestion
Bringing data from its sources into a system where it can be stored, processed and analysed, whether in batches or as a live stream.
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Data Governance
The policies, roles and controls that decide who can access data, how it is kept accurate and how its use is tracked.
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data-consistency
Data Consistency
Data that stays correct and in agreement across copies, transactions and systems, so every reader sees the same truth.
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data-balance
Data Balance
How evenly examples are spread across classes or conditions in a dataset; imbalance teaches a model to favour whatever it sees most.
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cross-validation
Cross-validation
Evaluating a model by splitting the data into several folds, training on some and testing on the rest in turn, to get a more honest accuracy estimate.
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cosine-similarity
Cosine Similarity
A score between -1 and 1 for how closely two vectors point in the same direction, used to compare embeddings regardless of their length.
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Convolutional Neural Network (CNN)
A neural network built for images: it slides small filters over the picture to detect edges, textures and shapes, then combines them into higher-level features.
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connected-components
Connected Components
Groups of items linked by similarity so that every member connects to the group through at least one close neighbour, used to find clusters and duplicates.
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compliance
Compliance
Meeting the laws, standards and internal policies that govern how data is collected, stored, used and audited.
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collaboration
Collaboration
People and teams working on shared data, models and decisions with a common view of the work, rather than in separate tools and silos.
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Software designed from the start to run in the cloud, typically built as containers and services that scale and recover automatically.
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cloud-computing
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Renting computing resources such as servers, storage and databases over the internet, scaling up or down on demand instead of owning hardware.
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Cloud AI
Running AI training or inference on a provider's infrastructure rather than your own hardware, paying for compute as you use it.
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CLIP
A model from OpenAI that learns images and text in one shared space, so a sentence can be matched to pictures it describes without task-specific training.
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caching
Caching
Keeping a copy of frequently used data in fast storage so later requests are served without recomputing or refetching it.
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Buy vs Build
The decision between purchasing an existing product and developing your own, weighing speed, cost, control and long-term maintenance.
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Business Value
The measurable benefit a project delivers to the organisation: revenue, cost saved, risk reduced or time recovered.
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Black-box Model
A model whose internal reasoning cannot be inspected or explained, so you can see what it predicts but not why.
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big-data
Big Data
Data too large, fast or varied for traditional tools to store and analyse, which calls for distributed storage and processing.
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